Balancing Innovation with Prudent Risk Management
In finance, AI can transform fraud detection, compliance reporting, risk assessment, and customer personalization—but only if adopted securely and compliantly.The first four weeks must build a foundation that accelerates value while hardening defences against new threats like prompt injection, model poisoning, or regulatory non-compliance. As of January 2026, with the EU AI Act's high-risk rules (covering creditworthiness, fraud detection, and insurance underwriting) approaching full application in August 2026, and the FCA emphasizing explainable AI, accountability under SM&CR, and outcomes under Consumer Duty, early governance is non-negotiable.
Week 1: Identifying Opportunities, Launching Trials, and Establishing Governance
Focus on assessment, quick trials, and immediate governance setup to avoid downstream rework.
- Map Pain Points and Opportunities:
Conduct a cross-functional workshop (compliance, risk, IT, legal, security) to identify inefficiencies—e.g., manual AML/KYC reporting or fraud false positives. Prioritize five high-ROI areas: AI fraud detection, customer service automation, insurance underwriting optimization, personalized marketing, and compliance reporting automation.
- Launch Trials Safely:
Select tools with trial periods (e.g., ComplyAdvantage for compliance, Azure AI for custom agents). Test in isolated environments.
- Establish AI Governance from Day 1:
Form a lightweight AI Governance Working Group (CISO, Head of Compliance, Legal, Data Privacy, business leads, and a Senior Manager accountable under SM&CR).
Define basic Responsible AI principles: fairness, explainability (XAI), bias mitigation, transparency, and accountability.
Classify use cases by risk tier (high-risk for credit/fraud/compliance decisions per EU AI Act Annex III).
Align with FCA expectations for outcomes-focused oversight and prepare for EU AI Act high-risk requirements (e.g., risk management systems, technical documentation, human oversight) ahead of August 2026 deadlines.
By Day 7, have trials initiated and governance charter drafted—preventing "bolt-on" compliance later.
Weeks 2-3: Secure Implementation and Integration
Execute with security-by-design and regulatory alignment baked in.
- Define Scope and Select Tools:
Detail tasks (e.g., AML report generation). Involve the AI Governance Group to assess regulatory classification—many finance AI uses (fraud detection, credit assessment) qualify as high-risk under the EU AI Act, requiring conformity assessments, data governance, and post-market monitoring by mid-2026.
- Integrate Data Sources Securely:
Connect via encrypted APIs to CRMs/transaction systems. Implement robust anonymization/pseudonymization, test for re-identification in sandboxes, and enforce data residency rules. Conduct AI-specific security controls: guardrails against prompt injection, input/output filtering, and monitoring for exfiltration. Vet third-party vendors rigorously—require SOC 2 reports, AI-specific addendums for model transparency/training data disclosure, and contractual clauses on updates/sub-processors.
- Train, Test, and Deploy (Days 15-21):
Fine-tune with domain data using RAG. Prototype one report type, then deploy phased (pilot team first). Perform AI red-teaming (simulated attacks) and bias/fairness testing during validation. Ensure audit trails for explainability and human-in-the-loop for high-stakes outputs.
Week 4: Risk Mitigation, Monitoring, and Cultural Readiness
Shift to refinement, learning, and hardening.
- Avoid Common Pitfalls:
Overlooking nuanced regulatory interpretations (e.g., subtle FCA Consumer Duty fairness in AI decisions); inadequate anonymization exposing PII; model drift amid regulatory changes (e.g., new FCA guidance or EU AI Act standards). Add: failing to address AI cyber risks (deepfake phishing, model inversion attacks) or third-party dependencies.
- Implement Ongoing Controls:
Set up dashboards for model drift, bias metrics, and AI-specific threats (e.g., unusual agent behaviour). Require human review for regulated outputs. Monitor vendor AI updates and conduct periodic red-teaming.
- Build Talent and Cultural Readiness:
Run AI literacy sessions for compliance/risk teams—emphasize "AI augments experts." Address resistance in legacy environments through clear messaging and upskilling plans. Hire or train AI-savvy security/compliance roles.
- Define Risk-Adjusted Success Metrics:
Beyond time saved, track false positive reduction without undetected fraud increase, audit trail completeness, bias/fairness scores, and compliance incident rates. Align with FCA SM&CR accountability and EU AI Act documentation needs.
- Learn from Industry Examples:
JPMorgan Chase excels in fraud AI and document processing (COiN), saving massive hours. Upstart scales lending via alternative data with controlled risk. Failures like Knight Capital ($440M algo loss from poor testing) and Zillow ($500M write-down from drift) underscore testing, governance, and drift monitoring.
A Secure, Sustainable Path Forward
The first 30 days of AI adoption in finance must fuse innovation with defensive rigor. By integrating governance, security hardening, vendor diligence, and 2026 regulatory foresight (EU AI Act high-risk prep, FCA principles via Consumer Duty/SM&CR), legacy firms can capture efficiency gains while safeguarding trust and license to operate.
Post-30 days, expand thoughtfully—predictive features, agentic AI—with continuous monitoring and annual reviews. AI augments expertise, never replaces it. Measure holistically, adapt to evolving regs (watch FCA AI Live Testing cohorts and EU sandboxes), and collaborate cross-functionally. This blueprint turns the first month into a resilient launchpad for long-term, regulator-aligned success.
If your firm is in London, consider engaging FCA initiatives (e.g., AI Lab testing) early—they offer practical support for safe deployment.
Dubai, UAE — January 13th 2026 — TheImpact.ae today announced the appointment of Trevor Coyne as Chairman, alongside confirmation that the business has formally separated from theimpact.team and will rebrand in Q1 as VerityX.
The appointment and rebrand mark a decisive shift in the company’s positioning — from advisory-led digital transformation to execution-first delivery across digital, AI and operational modernisation, particularly within regulated and complex environments and across Europe, Middle East and Africa.
Trevor Coyne is the Founder and CEO of OpsTalent, a global operations and technology outsourcing firm with delivery hubs across UK and EMEA. OpsTalent provides scalable, multilingual operational services, technology delivery, and execution capability for enterprise clients.
Through Trevor’s appointment, VerityX gains direct access to OpsTalent’s global delivery engine, significantly strengthening its ability to translate strategy into secure, scalable execution.
Mark Rothwell-Brooks, CEO of theImpact.ae and incoming VerityX, commented: “This appointment and rebrand are intentional. The market does not need more transformation advice — it needs execution that works in real operating environments. Trevor brings deep operational leadership, and OpsTalent brings delivery at scale. VerityX is being built to close the gap between ambition and outcomes, particularly for banks, governments and regulated organisations.” See for further details https://www.theimpact.ae/verityx
Trevor Coyne added: “VerityX represents a sharper focus on execution. By combining transformation leadership with a proven global delivery model, clients gain the confidence that change will not only be designed — but delivered.”
The rebrand to VerityX will be completed during Q1, establishing the firm as a standalone entity with a clear mandate around:
• Digital and AI execution at scale
• Operating model readiness and delivery assurance
• Regulator-aware transformation
• Access to global talent and development capacity
About VerityX
VerityX (formerly TheImpact.ae) is a digital and AI execution firm focused on helping banks, governments, and regulated enterprises turn strategy into measurable outcomes.
https://www.theimpact.ae/verityx
About OpsTalent
OpsTalent is a global operations and technology outsourcing partner delivering multilingual operations, software development, and execution services across UK, Europe and the Middle East.
In the fast-moving world of artificial intelligence, we're surrounded by what feel like "shiny new toys"—powerful tools promising to transform how we live, work, and interact. Yet the real magic isn't in the raw capability of these models; it's in how designers make them feel intuitive, joyful, and human. From Jony Ive's minimalist ethos at Apple to his current push at OpenAI for elegant, screen-less AI devices, good design turns complex tech into something that delights rather than distracts.
The challenge? Many AI integrations still feel like gimmicks—intrusive, error-prone, or overwhelming. The best ones disappear into the background, enhancing experiences seamlessly. Here's how we've arrived here, where we've gotten it right, and where the future might take us.
Evolution of AI Interfaces
AI conversational interfaces trace back to the 1960s with ELIZA, a simple text-based "therapist" that mirrored user statements in dialogue form. Despite no real understanding, users formed emotional connections simply because the back-and-forth felt natural.
Decades later, this linear, chat-style format persisted through rule-based bots and messaging apps like WhatsApp or iMessage. When large language models exploded with ChatGPT in 2022, OpenAI chose an ultra-simple single-page chat: scrolling history, text input at the bottom, minimal controls.
This design won because it mimicked everyday texting—zero learning curve, preserved context across turns, and instant accessibility. Grok and others followed suit, adding personality while keeping the core simplicity. The result: AI feels like talking to a smart friend rather than wrestling with software.
AI Integration in Apps and Webpages: The Good and the Bad
Today, AI embeds everywhere, from predictive recommendations to automated workflows. When done well, it anticipates needs and personalizes without fanfare.
Spotify's hyper-personalized playlists analyse habits to curate content that feels hand-picked, boosting engagement through effortless discovery.
Google's real-time conversational search updates dynamically as you speak or type, turning queries into natural dialogue.
Failures stand out just as clearly: intrusive chatbots that loop endlessly, hallucinated summaries that spread misinformation, or privacy-violating companions that feel creepy. Overly proactive features add notification fatigue, while biased algorithms erode trust.
The difference? Successful integrations prioritize subtlety, transparency (showing reasoning when needed), user control, and reliable fallbacks to human help. Poor ones chase hype over tested user experience.
Designing for Trust in the AI Era
Beyond subtlety and control, 2026 demands ethical guardrails—transparent reasoning in AI decisions, proactive bias audits, and inclusive interfaces (like multimodal voice for accessibility). When designers bake in observability and human fallbacks from the start, AI stops risking distrust and starts earning loyalty as a reliable, fair assistant.
AI in Finance and Fintech
Finance shows both the promise and pitfalls of AI at scale. The sector's AI market hit around $30 billion in 2025, fuelled by agentic systems that act autonomously on tasks like fraud detection, personalized advice, and compliance.
Leading banks like JPMorgan Chase embed generative AI for risk analytics, underwriting, and hyper-personalized services, delivering efficiency gains and productivity boosts through disciplined, governed rollouts.
In 2026, the real game-changer is agentic AI—systems that don't just respond but anticipate and act. Leading institutions are deploying these for autonomous workflows, like real-time compliance checks across borders or predictive portfolio adjustments, all while maintaining 'trust-by-design' governance to avoid biases or hallucinations. This moves AI from a tool to a proactive partner, dramatically improving CX by reducing friction and building deeper trust.
Fintech super-apps integrate AI across payments, investments, and more for unified, seamless experiences. Fraud systems cut manual reviews dramatically, while predictive personalisation tailors offerings in real time.
Challenges remain; biases in credit scoring, privacy risks, and hallucinations in visible outputs. Success comes from "trust-by-design"—explainable models, strong governance, and human oversight—turning AI into a reliable teammate rather than a risky black box.
The Future of AI Design: Hardware and Beyond
The next leap may move beyond screens entirely. Jony Ive, after Apple's iconic era, joined OpenAI following the 2025 acquisition of his startup io. His team is crafting a "family of products" that prioritize whimsy, calmness, and simplicity—screenless, audio-first devices that blend into life without social disruption.
Rumoured prototypes include a pen-like gadget (codenamed "Gumdrop") for capturing handwriting and voice notes that feed directly into AI, or compact pendants for ambient, context-aware assistance.
Recent developments point to a heavily audio-focused first device—leveraging OpenAI's Q1 2026 audio model upgrades for truly natural, interruption-handling conversations. This could redefine CX by making AI ambient and voice-driven, allowing proactive assistance (e.g., real-time budgeting whispers or fraud alerts) without pulling users into apps—echoing fintech's push toward seamless, context-aware personalization.
Ive emphasizes "chipping away" at excess for elegant minimalism—devices that inspire joy, feel playful, and make AI proactive yet non-intrusive. This echoes his Apple philosophy: create tools that empower and delight, not dominate attention.
In the AI era, shiny new toys lose their shine fast if they ignore human-centred design. The winners—whether chat interfaces, personalized apps, fintech agents, or tomorrow's ambient hardware—succeed by being invisible assistants: intuitive, trustworthy, and empowering.
Designers hold the key: prioritize empathy, subtlety, privacy, and genuine delight over flash. When AI feels like an extension of human intent rather than a flashy gadget, it stops being a toy and becomes something truly transformative. The era's best experiences aren't about the tech shining brightest—they're about making users feel smarter, calmer, and more capable.
The glass at the top of the Burj Khalifa does a curious thing at dusk. It turns the city into an idea. The highways soften into lines, the towers into propositions, and the water—whether one insists on calling it the Arabian Sea or the Arabian Gulf—becomes a patient constant, indifferent to ambition. From this height, Dubai looks less like a place than a thesis: that speed, scale, and intent can coexist, if only the operating model is sound.
It was here that Mark Rothwell-Brooks and Trevor Coyne met—not to announce anything (that would come later), but to agree on something far less ceremonial: that the era of transformation as theatre had ended, and the era of delivery as discipline had arrived.
For years, Mark had watched organisations across EMEA rehearse the same play. Act One: a declaration of intent—cloud-first, data-led, AI-enabled. Act Two: pilots that dazzled in committee rooms and demos that impressed precisely the people who already believed. Act Three: a long pause, during which risk committees asked questions no one had prepared for and operating teams wondered who would actually run the thing. The applause came early; the exits were crowded.
Trevor, meanwhile, had been sitting closer to the engine room. At OpsTalent, he saw what happened after the slides were approved—what it took to staff, run, secure, and sustain digital operations across borders and languages, under regulators who preferred evidence to enthusiasm. “Most initiatives don’t fail because the idea is wrong,” he said, looking down at the city. “They fail because the organisation assumes execution will take care of itself.”
From above, the distance between those perspectives vanished.
The trouble with ambition
EMEA is having a moment. Regulators are more sophisticated, not less. AI has moved from novelty to inevitability. Governments are digitising services while insisting—correctly—on sovereignty, resilience, and control. Banks are under pressure to do more with less, faster, and without incident. The ambition is real. So are the constraints.
What has changed is patience. Boards now ask a simpler question than they once did: What is live? Regulators ask a sterner one: Who is accountable? CFOs ask the most dangerous question of all: What did we get for it?
Mark had heard these questions enough times to know what they were really asking. Not for more strategy, but for proof that strategy could survive contact with reality. “The market doesn’t need another transformation narrative,” he said. “It needs an operating model that can carry the weight.”
Why partnerships fail—and why this one didn’t
The city below offered its own lesson. Dubai doesn’t work because it has the tallest buildings. It works because power, water, transport, and regulation are designed together. You can’t outsource gravity.
Too many transformation efforts, Mark and Trevor agreed, are assembled from parts that never meet: advisors who design, integrators who build, outsourcers who run—each optimised for their own contract, none accountable for the outcome. The handoffs are where momentum goes to die.
This was the problem VerityX—newly named, deliberately so—was created to address. To separate from theimpact.team was not an act of abandonment but of clarity. Focus matters. Execution requires it.
VerityX would be built to do the unglamorous work early: designing delivery into the strategy, governance into the workflow, and operations into the architecture. OpsTalent would bring what most strategies lack: a delivery engine that wakes up every morning and runs the thing—multilingual, regulated, and at scale.
“Partnership only works when incentives align,” Trevor said. “If one party wins at the point of sale and another inherits the risk, the system breaks.” From the height of the Burj Khalifa, that seemed obvious. On the ground, it rarely was.
AI, without the magic
If digital transformation had been guilty of overpromising, AI was in danger of repeating the offence—only faster. Everyone had a pilot. Few had production systems with monitoring, controls, and humans in the loop. The models were clever; the operations were brittle.
“AI doesn’t fail because it isn’t intelligent enough,” Mark said. “It fails because organisations underestimate what it takes to run intelligence safely.”
Here, the partnership showed its sharpest edge. VerityX would focus on AI execution: embedding models into real workflows, defining accountability, designing controls regulators could understand. OpsTalent would provide the operational backbone—support, monitoring, multilingual operations—without which AI remained a demo.
It was not anti-innovation. It was anti-illusion.
The view from above, the work below
As the light faded, the city resumed its scale. From this height, it was easy to believe that everything moved smoothly. Up close, it was all friction and effort. The point, Mark and Trevor agreed, was not to eliminate friction—that was impossible—but to design systems strong enough to move through it.
That, ultimately, was why the timing mattered. The market had matured. The questions had sharpened. The tolerance for theatre had evaporated. What remained was execution.
Below them, Dubai continued to do what it does best: make the improbable operational. Above the pilot phase, above the slides, above the noise, a partnership had formed around a simple idea—that delivery is a design choice, and now was the moment to choose it.
VerityX, partnered with OpsTalent, would step into that space—not promising transformation, but accepting responsibility for it.
And from the top of the tallest building in the world, that felt less like ambition than inevitability.
A few years ago, AI still felt like sci-fi. Now it’s just… Tuesday.
Models can pass exams, write code, solve your kid’s maths homework, and spit out film-quality images and video from a two-line prompt. The glossy AI assistants we used to see in movies? They’re basically here.
And the honest answer to “Can I have my own?” is: yes. If you’ve got the right hardware, anyone can. That’s because of a flood of open-weight and open-source models you can download, run locally, and fine-tune on your own data for very specific jobs.
That part is already happening at scale.
But before we get into the fallout, it’s worth being precise about what we’re talking about.
People tend to mash these terms together, but they’re not the same thing.
Right now, the energy – and the risk – sits with open weights, not true open source. There are no genuinely frontier-class fully open-source models in the wild. But there are plenty of powerful open-weight ones you can grab today.
Because they’re so capable and so widely available, they’re creating very real, very current problems.
Let’s be fair: this isn’t all doom.
The open-weight boom has enabled a legitimate ecosystem of companies that specialise in slices of the AI stack instead of burning billions training their own frontier model.
In other words: open weights have democratised access to serious AI. You no longer need to be OpenAI-sized to build something impressive.
But the exact same openness that powers all this innovation also enables something much darker.
So what happens when the same models are pointed, deliberately, at harm?
Picture an election year.
Your feed is full of angry, emotional posts. Hundreds of thousands of accounts hammering the same talking points, sharing “leaks,” pushing clips that feel designed to make you furious. It looks like a huge grassroots movement.
It doesn’t have to be.
It could be one well-funded organisation running a customised open-weight model as a digital propaganda engine. That system spins up and manages an army of fake accounts, each with its own personality, posting schedule, and language style.
The AI understands context. It replies to comments with tailored arguments. It cites “sources” that look credible unless you dig several layers deep. It’s tuned to ride right up to the edge of platform rules – just toxic enough to shift opinion, not quite bad enough to trigger bans.
And then it goes further.
These systems can generate deepfake video and audio in local accents, mirroring the slang, humour, and cultural cues of the exact group they’re trying to influence. They can scrape your public social media and run hyper-personalised psychological operations against you and people like you.
At that point, this isn’t just spam. It’s cognitive warfare.
Traditional propaganda tries to change what you think, whereas cognitive warfare aims to change how you think.
It exploits bugs in the human operating system: our biases, our fear of missing out, our tendency to trust familiar faces, our inability to fact-check a firehose of information in real time. The goal isn’t just to sell you a story – it’s to erode your ability to trust anything.
And open-weight AI is the missing piece that makes this scalable.
For years, this kind of operation was constrained by human effort. You needed legions of trolls, content farms, and call centres. Now, one well-engineered system can impersonate thousands of “real people” at once, 24/7.
We’re not theory-crafting. We’re already seeing early versions of this.
United States: the “phantom” candidate
In a recent US election cycle, voters received robocalls where President Biden apparently told them not to vote. It sounded like him. It wasn’t. It was a cheap AI voice clone that still required federal action to shut down.
At the same time, a Russian “Doppelganger” campaign went beyond fake articles. It used AI to recreate the entire look and feel of major news sites – think cloned versions of The Washington Post or Fox News – and filled them with anti-Ukraine stories that looked indistinguishable from the real thing at a glance.
Russia–Ukraine: the first AI war
Early in the invasion, hacked Ukrainian TV stations briefly ran a video of President Zelenskyy at a lectern, instructing his troops to surrender.
It never happened. It was a deepfake.
By today’s standards it was clunky, but it proved a chilling point: you can hijack the face and voice of a head of state and use it to try to break a country’s will.
Israel–Palestine: the “liar’s dividend” in action
During the Israel–Gaza conflict, reality and fabrication began to blur completely.
The “All Eyes on Rafah” image — a pristine, AI-generated camp scene — went mega-viral, shared tens of millions of times, shaping emotion and opinion around an event that never looked like that.
At the same time, genuine images of horrific violence were dismissed by many as “AI fakes.” That’s the liar’s dividend: once the public knows deepfakes exist, anyone can claim that inconvenient real footage is “just AI.”
The weapon is no longer just the fake. It’s the collapse of trust in anything that looks like evidence.
Major powers have noticed.
Open-weight models are being folded into state-sponsored influence operations to build what some analysts call “synthetic consensus”: flood the information space with bots until fringe views feel like the majority.
Examples:
This isn’t sci-fi. It’s already part of the day-to-day information environment.
If disinformation is the visible side, cybersecurity is the quiet, arguably more dangerous flank.
Open-weight models are force multipliers for attackers. A small Advanced Persistent Threat (APT) group no longer needs a floor of elite hackers. With the right model and training data, they can:
What used to take months of R&D and significant money can now be packaged into “Crime-as-a-Service” offerings on the dark web.
We’re already seeing products like WormGPT, FraudGPT, and DarkBERT – open-weight models fine-tuned on criminal data and sold on subscription. They help criminals write better scam emails, build more convincing fraud sites, and automate parts of their attacks.
Open weights have effectively put advanced offensive capability on the shelf.
Different regions are approaching this tension between “open” and “safe” in very different ways.
European Union & United States
China
China has chosen a very different path: tight domestic control, aggressive global release.
Open weights have become a geopolitical instrument, not just a technical choice.
Once you accept that powerful open-weight models are out in the wild, you’re forced into a new kind of realism.
You can’t regulate them out of existence without cutting yourself off from the global AI economy. They are essential building blocks for local innovation – the only way many countries and companies can realistically build AI tailored to their own languages, laws, and industries.
But that open door lets a cold wind in.
The same tools that power local startups and research labs also give small, hostile teams the ability to run operations that once required nation-state level capability. The buffer between “breakthrough” and “weapon” has basically disappeared.
So where does that leave policymakers and builders?
The focus can’t just be “who’s allowed to download a model” anymore. That ship has sailed.
The priority has to shift from controlling access to building resilience:
We’re not going back to a world where only a handful of companies can train or run powerful models.
The question now is whether we grow the immune system to match the power of the tools we’ve just handed to everyone.
In an era where AI and cloud technologies promise to redefine financial services—projecting $67 billion in global AI spending by 2028—why do 70-88% of digital transformations still fail? The answer lies not in code or clouds, but in human blind spots: misaligned incentives, cultural inertia, and overlooked fears. Drawing from decades of frontline experience and recent analyses, this white paper reimagines 20 enduring truths as a roadmap for learning from past pitfalls.
What if your next initiative didn't just digitise processes but ignited purpose? Backed by case studies from the Gulf, Europe, and Africa, we explore how embracing these lessons can turn transformation from a costly gamble (with $2.3 trillion already burned globally) into a catalyst for 30% higher returns on equity. Imagine institutions where clarity fuels innovation, resistance reveals insights, and AI amplifies human potential. This is your invitation to evolve—not react.
Picture this: A Gulf bank pours billions into AI governance, only to watch adoption stall because middle managers feel sidelined. Or a European insurer's cloud migration unravels amid unspoken fears of job loss. Sound familiar? In 2025, financial institutions face an inflection point. AI is reshaping operations—from fraud detection (reducing false positives by 40%) to hyper-personalised client experiences—while cloud adoption promises 20-40% cost savings. Yet, McKinsey reports only 30% of transformations succeed, with 88% falling short of ambitions per Bain's 2024 analysis.
Why the disconnect? Traditional mistakes—overreliance on tech without human alignment—persist. BCG highlights employee resistance as a top culprit in 70% of failures, echoing the TSB Bank's 2018 IT migration debacle, which cost £330 million and eroded trust overnight. But failure isn't fate. As Mashreq Bank in the UAE demonstrated, integrating human-centred strategies with digital tools spurred 25% revenue growth through seamless mobile banking.
This white paper distills 20 human truths from rescued transformations, reimagined for the AI-cloud nexus. We ask provocative questions: What if resistance was your best consultant? How might vulnerability from leaders unlock psychological safety? Supported by 2025 insights, these lessons inspire a bolder path: from compliance to confidence, where technology serves humanity, not supplants it.
Leaders, ask yourself: Does your governance stifle speed or safeguard it? Traditional traps like excessive sign-offs have doomed 50% of projects to timeline overruns. Truth 1: Clarity trumps control. A Middle Eastern bank's shift to weekly "clarity sessions" slashed duplication by 40%, mirroring successes like Standard Chartered's blockchain KYC platform, which cut compliance times by 60%.
Truth 2: Leaders who shield comfort sabotage change. When a European CEO micromanaged agile requests, innovation flatlined—much like Co-op Bank's $1.9 billion core-system fiasco, where executive detachment amplified chaos. In contrast, Banorte's multi-year AI-cloud rollout, starting with vulnerability-sharing forums, halved escalation times and boosted adoption.
Truth 3: Pace psychological safety from the top. Fear breeds silence; a "Friday Failure Forum" in one bank turned red flags into rapid learning, aligning with Deloitte's 2026 outlook: AI scales only on safe foundations.
Envision leaders modelling AI experimentation—not as threat, but as ally. What breakthroughs await when discomfort becomes your competitive edge?
Culture: Is it your mission statement or the behaviours you ignore? Truth 4: What you tolerate defines tomorrow. A Gulf insurer's unchecked delays eroded trust until "leadership audits" reversed course, echoing Africa's pan-African bank's leadership program that prototyped instant onboarding, cutting costs by 30%.
Truth 5: Data's honesty mirrors your culture. Sanitizsed metrics in a European bank hid risks, per McKinsey's warning: Fragmented data throttles AI. Reward transparency, and watch AI-driven insights flourish— as in EY's report, where GenAI streamlined loan processing, saving 20-40% in costs.
Truth 6: Amplify quiet voices over loud ones. Frontline insights via "idea dividends" yielded breakthroughs, countering the 48% of banks where silos mute data flow.
Truth 7: Legacy hides in habits. Weekly "lessons learned" memos endured reorganisations, building resilience akin to Ireland's post-2008 playbook, now inspiring African digital leaps.
Imagine a culture where AI augments empathy: Employees, empowered, co-create the future. Doesn't that ignite possibility?
People fear loss, not change. Truth 8: Acknowledge it, then reframe gains. GCC teams embraced cloud automation when repositioned as "security elevation," boosting engagement—unlike TSB's glitches that locked out millions, fuelling distrust.
Truth 9: Resistance is free consulting. UAE "resistance roundtables" converted pushback to progress, aligning with BCG's call: Engage employees to flip 70% failure odds.
Truth 10: Middle managers are your momentum makers. Integrating them in sprints cleared dependencies in a North African bank, per CCL's case: Leadership development accelerated prototypes like secure trade platforms.
Truth 11: Silence signals disengagement. A data rollout's quiet hid abandonment—probe it, as Salesforce's AI tools do, personalising nudges to lift adoption 200%.
What if fear became fuel? In this new era, AI frees humans for purpose—fraud hunters spotting real threats, advisors crafting legacies. Thrilling, isn't it?
Strategies reveal themselves in pilots. Truth 12: Safe ones expose performative intent. Synthetic data in an AI trial screamed caution; real-risk experiments, like JPMorgan's JPM Coin, slashed cross-border times.
Truth 13: Unanimity masks untruths. Green-light theatre preceded a regulator's shock—foster conflict, as Deloitte urges for AI governance.
Truth 14: Measure adoption, not just delivery. Behaviour shifts define success; WalkMe notes 70% fail sans engagement.
Truth 15: Meaning moves mountains over metrics. Linking automation to threat detection soared motivation, per RGP's 85% AI adoption in risk modelling.
Truth 16: Urgency sans direction exhausts. 37 competing initiatives burned out a global bank; focus, as Accenture advises, turns cloud-AI into growth engines.
Truth 17: Governance is velocity's guardian. Traceable decisions built auditor trust in one institution, enabling faster AI scaling.
Truth 18: Brilliance brews in boredom. Metadata cleanup preceded flawless models—patience pays, as Rackspace's AML GenAI cut reporting 60%.
Truth 19: Adoption earns daily. "Small wins" celebrations surged engagement, sustaining change beyond
go-live.
Ponder: What if your strategy wasn't a sprint, but a symphony? Truth 20: AI and cloud harmonise when humans conduct—yielding not just efficiency, but enduring excellence.
Transformation isn't technological triumph; it's emotional evolution. From Gulf cloud migrations to African fintech frontiers, successes like Gulf Bank's Backbase-powered digital shift prove: Human truths + AI = unbreakable momentum. We've learned the hard way—70% failures from ignored humans—but now, with $97 billion in AI bets by 2027, the stakes inspire action.
You hold the blueprint: Clarify intent, embrace discomfort, measure meaning. In this new era, financial institutions won't just survive—they'll soar, delivering trusted, intuitive services that redefine prosperity. What truth will you act on today? The future of finance awaits your inspired lead.
For partnership in AI-cloud transformations, contact The Impact Team at www.theimpact.ae. Accelerate with certainty.
Across the GCC, banks and financial institutions have embraced generative AI with speed and enthusiasm. Developers are using code assistants, leaders are demanding productivity gains, and transformation programmes increasingly include “GenAI enablement” as a visible workstream.
Across the GCC, banks and financial institutions have embraced generative AI with speed and enthusiasm. Developers are using code assistants, leaders are demanding productivity gains, and transformation programmes increasingly include “GenAI enablement” as a visible workstream.
But the truth is clear: high adoption has not yet converted into high impact. Most organisations are still stuck in pilot mode — achieving small pockets of efficiency without translating them into material business value.
This briefing outlines what the market is learning, and what banks and regulators in the region need to do next.
While two-thirds of software organisations now use GenAI tools, the measurable gains remain modest — typically 10–15% efficiency improvements at the individual
developer level.
Most banks still run legacy development processes, heavy governance cycles, and disconnected teams. This means any time saved through AI is simply absorbed into existing inefficiencies — never flowing through to faster delivery, reduced risk, or improved compliance outcomes.
GenAI applied to old processes yields old results.
Financial institutions in the region operate under three pressures that magnify the problem:
Basel, cyber resilience, AI governance, model risk, and new supervisory frameworks require faster, more consistent software releases — not sporadic AI-assisted coding.
GenAI cannot generate value if upstream architecture, testing, security reviews and change controls still operate in slow, manual ways.
Banks must ensure any AI usage is tightly governed, explainable, and fully auditable — not a free-form experiment at the developer’s desk.
These realities mean the GCC cannot rely on tool-level adoption alone. The sector must move towards AI-native engineering, where GenAI is woven into processes, not layered loosely on top.
The institutions beginning to see meaningful payoff treat GenAI as a transformation catalyst, not a tactical enhancement.
They commit to:
Embedding AI across design, architecture, coding, testing, security, documentation, and deployment.
Removing redundant handoffs, compressing review cycles, and structuring delivery around rapid iterations supported by AI accelerators.
Introducing AI firewalls, secure model access, granular entitlements, audit logs, and risk-aligned controls — enabling safe adoption at scale.
Reskilling teams, redefining roles, and creating norms where AI is a permanent co-worker, not a novelty.
The result isn’t just faster output — it’s better engineered, more secure, regulator-ready software, delivered consistently.
For banks, this is a chance to unlock real transformation:
For regulators, the shift is equally strategic:
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Generative AI will not transform your organisation because you have tools. It will transform your organisation when you redesign how you deliver software, how you govern technology, and how you align development with regulatory expectations. The Gulf’s most forward-looking banks are now moving decisively from pilots to payoff. With the right architecture, controls, and operating model, GenAI becomes not an experiment — but a competitive advantage.
Today we're diving into a topic that's set to reshape the entire banking landscape. How artificial intelligence is poised to transform the relationship between the regulators and the commercial banks. Gone are the days of periodic audits and quarterly reports. AI is ushering in an era of real-time supervision. Imagine regulators having a live pulse on a bank's operations, spotting risks before they snowball, and fostering a more collaborative dynamic. But what does this mean in practice? And how will it change the game?
In this episode, I'll break down the key areas where AI could revolutionize this relationship. We'll cover real-time monitoring, predictive risk assessment, automated compliance, enhanced communication, and even some potential pitfalls. Let's jump in.
First off, let's set the stage. Traditionally, bank regulation has been a bit of a periodic health checkup. Regulators like the Fed, the OCC, or international bodies such as the Basel Committee would swoop in for exams every year or so, pouring over mountains of data to ensure banks are playing to the rules, managing risks, maintaining capital buffers, and avoiding shady dealings. It's effective, but it's reactive and it's resource intensive. Very, very resource intensive. Banks submit reports, regulators analyse them, and if something's amiss, corrective actions should follow. So enter AI.
With advancements in machine learning, big data analytics, cloud computing, regulators can now tap into a continuous stream of information. We're talking about AI systems that process transactions, monitor liquidity, flag anomalies, and do so in real time. And this is not science fiction. It's already happening in pilots all around the world. For instance, the Bank of England has been experimenting with AI for supervisory tech or subtech, and the European Central Bank is exploring similar tools. So how exactly will this change the regulator bank relationship? So let's explore each of the key areas one by one.
Area number one, real-time monitoring and data sharing. Picture this: A bank's AI system feeds live data directly to a regulator's dashboard. And instead of waiting for a quarterly filing, regulators could see deposit flows, loan portfolios, even cybersecurity threats as they happen. And this creates a more intimate, ongoing relationship. Banks might feel like they're under constant watch, but it could also build trust. Why? Well, because early detection means smaller problems don't become crises. For example, during the 28 financial meltdown, regulators were blindsided by hidden risks in mortgage-backed securities. With AI, algorithms could scan for patterns in real time. Say a sudden spike in high-risk loans, alert both the bank and the regulator simultaneously. There's shifts a dynamic from adversarial to collaborative. Banks get instant feedback and regulators get transparency, and the whole system becomes more resilient. Of course, this requires secure data pipelines. Banks will need to share more granular data without compromising privacy or propriety information. Regulators, in turn, must invest in AI that's robust against hacks. It's a two-way street. It'll demand new standards for data governance. But now let's not get ahead of ourselves.
Moving on to area two, predictive risk assessment. AI excels at pattern recognition and forecasting. Regulators could use machine learning models to predict systemic risks before they materialize. Just think about stress testing. Currently, it's an annual ritual where banks simulate economic downturns. With AI, this could become dynamic. Algorithms could run continuous what-if scenarios based on live market data, geopolitical events, or even social media sentiment. This changes the relationship profoundly. Regulators won't just be referees, they'll be coaches providing proactive guidance. A bank might get an AI-generated alert saying, hey, your exposure to the commercial real estate is trending risky, adjust your capital now. Banks, in response, could integrate their own AI tools to align with regulatory expectations, creating a feedback loop. And this potential is huge. In Singapore, the monetary authority is already using AI for anti-money laundering surveillance, spotting suspicious transactions in seconds rather than days. This real-time edge means regulators can intervene surgically, reducing the need for broad punitive measures. But it also raises questions. What if the AI's predictions are wrong? Banks might push back, demanding transparency into the black box of these models. It's a shift from rule-based oversight to data driven partnership.
Now area three. Automated compliance and reporting. Compliance is the bane of every banker's existence. Endless forms, audits and paperwork. AI can automate much of this. RegTech solutions powered by natural language processing could generate reports automatically, ensuring they are accurate and timely. Regulators on their end could use AI to verify these submissions instantly, flagging discrepancies without human intervention. This streamlines the relationship, making it less bureaucratic and more efficient. And imagine a world where banks don't dread regulatory filings because AI handles the heavy lifting. Regulators freeing up resources to focus on the high-level strategy rather than the nitpicking details. So here's the visionary twist. This could evolve into compliance as a service. Banks might subscribe to AI platforms co-developed with regulators, embedding compliance checks into their core operations. It's like having a regulatory co-pilot. In the US, they're piloting AI for call report automation. It's a game changer. And what's the result? A closer, more symbiotic bond between banks and overseers, where compliance feels like collaboration, not just compulsion.
So, area four. Enhanced communication and interaction. AI isn't just about the data. It's about the dialogue. Chatbots, virtual assistants, AI-driven simulations could facilitate real-time Q ⁇ A between the banks and the regulators. Need clarification on a new capital rule? Well, ask the AI regulator bot trained on thousands of precedents. This fosters a more responsive relationship. Instead of waiting for weeks for a response to a query, banks get instant insights. Regulators could use AI to simulate bank behaviors, training their teams on potential scenarios. It's like war gaming, but just for finance. From my experience, this could reduce misunderstandings that lead to fines or indeed shutdowns or failures. In Europe, under the Digital Operation Resilience Act, AI is being leveraged for just this: real-time resilience testing. The upshot? Regulators become partners in innovation, helping banks navigate AI adoption themselves. Banks experimenting with AI for lending or fraud detection could get pre-approval through simulated regulatory views. And it's a win-win turning oversight into an opportunity. Of course, although digital transformation is done, and I've been on record by saying it's done, or more or less done, and with AI transformation being the new thing, it's key to stress that no transformation is without challenges.
So let's talk about area five, potential pitfalls and ethical considerations. While AI promises real-time harmony, it could introduce tensions, privacy being a big one. Banks might resist sharing sensitive data for good reason, fearing breaches or competitive leaks. So regulators must ensure AI systems comply with the relevant laws in the regions in which they operate, for example, GDPR. Then there's bias. If AI models are trained on flawed data, they could unfairly target certain banks or regions eroding trust. And I've seen this in consulting gigs where biased algorithms led to skewed risk assessments. And regulators need to audit their own AI for fairness. Over resilience is another potential risk. What if AI misses a black swan event like a pandemic or a cyber attack? Human judgment must remain in the loop. And let's not forget the digital divide. Smaller banks might lack the tech to keep up widening inequalities. So ethically, this real-time relationship could feel like big brother for the banks, stifling innovation if oversight becomes too intrusive. So regulators must balance vigilance with flexibility. And in my view, the key to this is co-regulation. Banks and overseers jointly developing AI standards, and it's about building a future where AI enhances, not erodes mutual respect.
Finally, area six, the broader future implications. Looking ahead, AI could redefine the very essence of bank regulation. We might see adaptive regulations, rules that evolve in real time based on AI insights. For instance, capital requirements could adjust dynamically to market volatility rather than just being static. This shifts the power dynamics profoundly. Regulators gain unprecedented visibility, but banks could leverage AI to negotiate better terms, using data to prove their stability. Internationally, it could harmonize standards. And imagine a global AI network where the Fed, the ECB, the People's Bank of China share anonymized risk data to prevent cross-border crises. And in the long term, this real-time relationship might even blur the lines between public and private sectors. Banks could embed regulatory AI into their boards, making supervision an internal function. It's visionary, but it's plausible. And the UAE are leading on this aspect right now. I believe we're on the cusp of a safer, more innovative financial system if we get the implementation right. And that's the key. If we get the implementation right.
Wrapping up, the introduction of AI into bank regulation for me is isn't just a tech upgrade. It's a relationship revolution from real-time monitoring to predictive analytics, automated compliance, dynamic communication, and beyond, AI promises a more proactive, efficient, collaborative future. But we must navigate the challenges. Privacy, bias, equity to ensure it benefits everyone. So what do you think? Will AI make regulators and banks unlikely allies or just introduce new frictions?
Drop me your thoughts in the comments or on social media. And if you're a banker or regulator tuning in, I'd love to hear your take.
You can subscribe to the podcast here. https://podcasts.apple.com/gb/podcast/the-impact-team-gulf/id1852204466
Large Language Models (LLMs) such as ChatGPT, Copilot, and Gemini represent a generational leap in enterprise productivity. They can accelerate software development, automate report writing, improve customer support, and drive operational insights at unprecedented speed. For financial institutions—where compliance, precision, and trust define the brand—these tools promise measurable efficiency gains.
Yet the very capability that makes LLMs powerful—their ability to understand, generate, and learn from human-like language—also introduces profound data-security, regulatory, and reputational risks.
Every prompt entered into a public LLM is, effectively, an outbound data transmission to an uncontrolled external system. When employees paste internal reports, source code, or personally identifiable information (PII) into such tools, the bank’s confidential data may leave its perimeter. In heavily regulated environments (e.g., GDPR, CBUAE Consumer Protection Regulations, PCI DSS, or APRA CPS 234), such actions constitute data breaches, even if unintentional.
This paper explores the tension between innovation and control, outlines the risk vectors unique to LLMs, and provides a pragmatic framework for adopting secure generative-AI capabilities within a banking environment. It also highlights the role of emerging technologies such as AI Firewalls—including solutions from vendors like Contextul.io—in enabling safe, policy-compliant LLM usage without stifling innovation.
Banking is an information-heavy industry. The ability to summarise, classify, and generate language-based artefacts has immediate applications:
· Software Engineering: Code completion, test generation, and documentation.
· Operations: Drafting policies, procedures, and audit responses.
· Risk & Compliance: Automating control narratives, mapping regulations to internal frameworks.
· Customer Service: Conversational chatbots capable of natural, context-aware responses.
· Data Analytics: Querying structured and unstructured data with natural language prompts.
McKinsey estimates that generative AI could add $200–$340 billion in annual value to the banking sector globally, primarily through productivity gains and faster time-to-market for digital initiatives.
In short, LLMs are no longer a curiosity; they are becoming an enterprise necessity. The challenge is to unlock this capability without creating new vectors of regulatory or reputational exposure.
Despite internal bans, employees across most large banks already experiment with ChatGPT, Bard, and Copilot. They use them to write meeting notes, refine documentation, or even debug code. This unregulated usage—Shadow AI—arises from good intentions: people simply want to work faster.
But every prompt is a potential data-loss event. Consider:
· A relationship manager pastes a client pitch deck to “make it sound more professional.”
· A financial controller asks ChatGPT to rephrase internal earnings commentary.
· A developer uploads production logs for debugging assistance.
· A compliance officer asks an LLM to summarise a Suspicious Activity Report (SAR).
In each case, proprietary or regulated data is transmitted to a public cloud endpoint outside the bank’s control, possibly stored or used for model retraining. Even when vendors claim not to retain prompts, assurance cannot be independently verified.
Such actions may breach:
· Data Protection Laws (GDPR, DIFC DP Law, PDPL in KSA, etc.) – particularly regarding data transfer and purpose limitation.
· Banking Secrecy Laws – prohibiting disclosure of client financial data.
· Internal Outsourcing Frameworks – since generative AI services constitute unapproved data processors.
· Third-party contractual obligations – e.g., non-disclosure agreements, embargoed financial results, etc.
Supervisory authorities (e.g., European Central Bank, CBUAE, PRA, APRA) have already emphasised that AI usage must comply with existing risk frameworks for outsourcing, operational resilience, and data protection. “Innovation” does not exempt compliance.
The majority of banks lack visibility into how employees interact with LLMs. Traditional Data Loss Prevention (DLP) and CASB tools are insufficient: they detect file movements and URLs, not prompt content or semantic risk. The result is an expanding “blind zone” where human creativity intersects with unmonitored AI interactions—an unacceptable position for regulated financial institutions.
Many banks have simply banned the use of public LLMs, blocking access at the firewall or proxy level. While this seems prudent, it produces three side effects:
1. Innovation flight: High-performing staff adopt personal devices or networks to bypass restrictions.
2. Talent frustration: Younger, digital-native employees perceive the organisation as outdated or bureaucratic.
3. Missed opportunity: Competing institutions that adopt controlled AI gain material efficiency advantages.
In practice, a ban creates risk displacement, not risk reduction. What is needed is a controlled adoption framework—a way to enable AI safely, visibly, and compliantly.
A resilient approach should rest on five principles:
1. Visibility: Know who is using what, and for what purpose.
2. Control: Enforce policy boundaries at the prompt level.
3. Containment: Prevent sensitive data from leaving the perimeter.
4. Transparency: Log and audit all AI interactions.
5. Enablement: Provide safe, sanctioned alternatives that actually work.
· Policy: Define acceptable use, data classification boundaries, and prohibited data types for AI systems.
· Roles: Appoint an AI Risk Officer and cross-functional AI Review Board (CISO, Legal, Data Protection, Model Risk, Audit).
· Lifecycle Management: Govern AI use like any other critical application—registration, risk assessment, monitoring, decommissioning.
· Training: Educate employees on what is safe to share, and why controls exist.
An AI Firewall—such as that developed by Contextul.io or similar vendors—acts as a policy-enforcing gateway between users and external LLMs. It monitors, classifies, and sanitises prompts and responses in real time.
Capabilities include:
Effectively, this creates a controlled conduit—allowing productivity gains while ensuring regulatory compliance.
Banks with advanced data platforms may deploy private LLM instances—either open-source (e.g., Llama, Mistral) or licensed proprietary models—within secure environments.
· Deploy within VPC or on-premises infrastructure.
· Fine-tune on internal documentation using approved datasets.
· Apply data classification filters before ingestion.
· Integrate with Identity & Access Management (IAM), DLP, and Security Information & Event Management (SIEM) systems.
· Support federated queries—so that internal LLMs can leverage approved external models via the AI Firewall.
Augment traditional DLP with semantic detection and contextual classification—recognising phrases, entities, or patterns that indicate risk even when data isn’t exact-match (e.g., “account number for client in Bahrain”). Modern AI Firewalls integrate directly with these tools.
Treat LLM providers as critical suppliers:
· Conduct due diligence (SOC2, ISO27001, CSA STAR).
· Require data residency transparency and model retraining opt-outs.
· Insert contractual controls: data deletion SLAs, audit rights, incident notification, and jurisdictional compliance (e.g., GCC data localisation).
Bank A (Global Tier 1)
Challenge: Shadow use of ChatGPT by 12,000 staff, causing regulatory concern.
Action: Deployed AI Firewall integrating with Microsoft Copilot, OpenAI API, and internal policy engine.
Outcome:
· 40% of previously blocked queries now safely processed via sanitisation.
· Zero data-loss events post-deployment.
· Employee satisfaction up 25% due to safe enablement instead of outright bans.
· Positive audit finding from internal risk committee.
Key performance indicators for secure LLM adoption include:
· Reduction in unsanctioned AI traffic (measured via proxy logs).
· Prompt policy compliance rate (approved vs blocked prompts).
· Incident volume (data leakage attempts detected and remediated).
· User enablement metrics (adoption of sanctioned AI tools).
· Time-to-approve new AI use cases (indicator of governance maturity).
Banks that treat AI enablement as a measurable operational capability—not just a policy—gain both control and agility.
1.. Acknowledge inevitability: Generative AI is not optional; it will permeate workflows. The question is not if but how safely.
2. Shift from prohibition to protection: Move beyond bans. Build controlled enablement with auditable guardrails.
3. Deploy an AI Firewall: Solutions like Contextul.io offer immediate, low-friction visibility and control over AI interactions.
4. Develop a unified AI policy: Align Legal, Compliance, Risk, and Technology. Clearly delineate responsibilities.
5. Integrate with enterprise security stack: Extend DLP, IAM, and SIEM to cover prompt-level telemetry.
6. Educate continuously: Provide mandatory training for all staff on AI risk, confidentiality, and safe usage.
7. Plan for incident response: Define escalation, forensic logging, and notification pathways for AI-related data incidents.
8.. Adopt privacy-by-design: Embed anonymisation, data minimisation, and consent logic into all LLM integrations.
9. Engage regulators early: Proactively disclose AI risk frameworks to supervisors—show governance maturity, not secrecy.
10. Pilot, measure, iterate: Start small, prove value, then scale with confidence.
In the near future, regulators will expect every major bank to demonstrate AI risk governance equivalent to existing operational resilience standards. Supervisory inspections will ask:
· Where are AI models used in production?
· How is data protected before, during, and after prompt submission?
· What independent assurance exists for AI vendors?
· How are decisions validated for bias or explainability?
Institutions that prepare now—embedding technical controls and governance discipline—will be positioned to leverage generative AI safely and faster than their peers.
Conversely, those that continue with blanket prohibitions will watch innovation move elsewhere. The competitive gap will widen, not just in cost efficiency but in culture, agility, and digital reputation.
Generative AI and LLMs offer the banking sector a profound opportunity to enhance productivity, automate complex processes, and personalise customer experience. But with that opportunity comes the duty to protect the institution’s most valuable asset: its data.
As CTOs and CISOs, our role is to create a bridge between innovation and assurance—to enable creativity without compromising compliance. The practical path forward lies not in restriction but in intelligent enablement: controlled access, monitored usage, and proactive governance.
Solutions such as AI Firewalls (e.g., Contextul.io) provide the technological foundation. Strong policies, disciplined culture, and leadership commitment complete the framework.
The banks that master this balance will not only avoid breaches—they will define the new standard for responsible, high-velocity innovation in financial services.
Fintechs promise speed, innovation and lower cost. Large banks prize resilience, control and regulatory assurance. The result is a persistent go-to-market gap: promising solutions stall in elongated sales cycles, InfoSec reviews, and onboarding mazes. This paper outlines why selling into major financial institutions (FIs) is hard, where the process typically breaks down, and practical steps both sides can take. It also highlights how initiatives like Finbridge Global aim to compress time-to-value by standardising due diligence, integration paths and commercial engagement.
Fintechs are optimised for rapid iteration; banks are optimised for risk control at scale. That cultural and operational mismatch shows up in four ways:
1. Timescales
o Typical enterprise buying journeys run 9–18 months from first meeting to production use—even longer for data-sensitive or customer-facing capabilities.
o “Pilot purgatory” is common: proof-of-concepts (PoCs) extend without a path to production, burning runway for the fintech and stakeholder goodwill at the bank.
2. Unwieldy processes
o Procurement requires multi-stage RFPs, competitive tension, and cross-functional approvals.
o Risk, legal, compliance and data-privacy reviews happen in parallel, each with different artefact needs and decision gates.
3. Onboarding friction
o Vendor onboarding includes financial viability checks, beneficial ownership, sanctions screening, cyber posture, BCP/DR testing, and often on-site (or virtual) audits.
o Access management (JML), data residency, encryption standards, key management, logging/monitoring and incident reporting mechanics must all align with bank policy—not just “industry best practice.”
4. Integration complexity
o Legacy systems, inconsistent APIs, and strict change-control windows complicate rollout.
o Non-functional requirements (latency, observability, failover, capacity planning) are as decisive as features.
· Ambiguous problem framing: If the bank cannot quantify the operational pain or regulatory exposure, the fintech’s ROI case remains abstract.
· Security documentation gaps: Missing pen-tests, incomplete SOC/ISO mappings, unclear data flows, or weak secrets management trigger rework and re-review.
· Misaligned commercial models: Start-up pricing tied to per-seat or MAUs may clash with bank budgeting; enterprise prefers predictable spend, outcome-based pricing, and flexible termination for regulatory cause.
· Change ownership uncertainty: Without a named production owner, run-book, and Level-2 support model, risk functions see operational fragility.
· Regulatory anxiety: New tech (e.g., AI) raises explainability, model risk, data lineage and third-country transfer concerns; banks default to “no” when controls are unclear.
For fintechs
· Enterprise-grade artefact pack:
Security whitepaper, data-flow diagrams, DPIA/ROPA drafts, encryption/KMS details, vulnerability management cadence, SBOM, pen-test summary, incident response playbook, BCP/DR evidence, and audit-ready logs.
· Bank-ready deployment options:
VPC-to-VPC, private link, on-prem/air-gapped options; clear SLOs, observability (metrics, traces, logs), and performance envelopes.
· Regulatory mapping:
Show how controls map to typical frameworks (e.g., outsourcing, operational resilience, cloud risk, model risk for AI).
· Commercial clarity:
Price tiers for PoC, pilot, and production with exit ramps; outcome or transaction-linked options; clear TCO comparison vs. status quo.
· Implementation recipe:
A step-by-step runbook for discovery → PoC → pilot → production, with artefacts, roles, and timelines (e.g., 4–6 weeks PoC; 8–12 weeks pilot).
For banks
· Single front door for fintechs:
A structured intake with standard artefacts and a triage SLA (e.g., 10 working days) to reduce random stakeholder hunting.
· Pre-approved control patterns:
Reference architectures, data-classification guardrails, and pre-agreed cloud patterns to avoid custom debates per vendor.
· Right-sized due diligence:
Risk-tier vendors and apply proportionate controls; reserve deep audits for material/critical suppliers.
· Time-boxed PoCs with production pathways:
Define success metrics, data scope, and a conversion plan before the PoC starts.
· Executive sponsorship and product ownership:
A senior sponsor to clear blockers and a named service owner to run BAU post-go-live.
Banks typically require the following before go-live. Fintechs that arrive “audit-ready” compress months of back-and-forth:
· Information Security: policy library, control matrix, SOC/ISO evidence, pen-test results, vulnerability SLAs, secure SDLC, secrets rotation, endpoint hardening.
· Data & Privacy: data inventory, classification, retention/erasure, encryption in transit/at rest, DPA terms, cross-border transfer basis, customer consent handling.
· Operational Resilience: recovery objectives (RTO/RPO), failover tests, capacity/DR drills, run-books, support tiers and escalation.
· Third-Party Risk: financial viability, insurance, subcontractor oversight, open-source license governance, SBOM.
· Legal/Commercial: negotiated liability caps, regulatory exit, audit rights, change control.
Platforms such as Finbridge Global seek to narrow the gap between fintech innovation and bank adoption by:
· Pre-vetting fintechs: Curating vendors against enterprise-grade criteria (security posture, compliance artefacts, operational maturity) to reduce first-line due diligence.
· Standardised artefacts: Providing templated security packs, DPIA scaffolds, control mappings and model-risk summaries—so banks review one consistent format.
· Regulatory alignment: Offering guidance on regional regulatory expectations (e.g., outsourcing, cloud, data transfer, AI governance), helping both sides speak a common control language.
· Faster procurement & onboarding: Facilitating structured intake, reference architectures, and integration runbooks that banks can adopt with minimal tailoring.
· Matchmaking with intent: Aligning bank problem statements to fintech capabilities and deployment constraints, avoiding generic “demo theatre.”
· Transparency & telemetry: Dashboards tracking PoC status, artefact completeness, and decision gates—creating accountability and momentum.
The net effect is a shorter path from first conversation to production, reduced compliance rework, and clearer commercial terms that fit enterprise budgeting models.
Selling fintech solutions into large banks is difficult—but not mysterious. Most delays stem from predictable gaps: unclear problem statements, inconsistent artefacts, misaligned commercials, and integration uncertainty. Fintechs that arrive enterprise-ready and banks that streamline intake and risk-tiering can convert months of friction into weeks of disciplined progress. Initiatives like Finbridge Global help both sides meet in the middle—standardising the artefacts, accelerating procurement and integration, and turning innovation into regulated, resilient production value.
The United Arab Emirates remains one of the most attractive destinations for international professionals, offering world-class infrastructure, tax-free salaries, and strong career opportunities. This makes for a strong and varied talent pool for employers. However, finding the candidate is often the easiest part; Onboarding can be a long and costly process if you do not have a clear understanding of the requirements.
We draw on our own experience of hiring in the UAE and outline the process, requirements and approx. costs to give you the tools to make sure you are ‘recruiting ready’ in good time.
It is important to know your legal obligations as an employer, prior to launching your recruitment drive as they may influence your decision to hire and how to hirecandidates. As an employer you are legally obliged to:
- Pay for the employee visa and EID which includes a medical test and biometrics capture
- Provide medical insurance to all employees
- Accrue an end of service gratuity each month from payroll
- Pay to cancel the work visa or employment card if the person leaves or is dismissed
- Pay for a return flight to the employee’s home country if the visa is cancelled
Every company has a set visa quota depending on where the company was incorporated – Free Zone or Mainland - and the visa quota is tied to the size of the business. Increasing that quota in a free zone can be costly and require the business to purchase additional office space. For mainland companies’ approval is granted on a case-by-case basis by MOHRE (Ministry of Human Resources) and this can take some time. Cost to increase visa quota in a free zone AED 15,000-30,000 and this is variable in the mainland depending on each specific situation.
The employment visa application process involves working with several government agencies, as well as either the Free Zone or the MOHRE depending on where the company is incorporated.
The smallest mistake or omission will result in rejection of the application and possibly additional costs to re-submit. The employment contract has to be registered with either the Free Zone or MOHRE before a visa can be issued and if everything is in order, the process, including the medical testing and biometrics capture will take between 10-20 days. The cost of a standard UAE employment visa for a two-year duration typically ranges from AED 3,000 to AED 7,000, plus AED 700 for medical and biometrics, although the total amount can vary significantly based on factors such as the employee’s job category, qualifications, urgency, the type of company (mainland vs. free zone), and the emirate. All fees are legally required to be paid by the employer.
Employers are legally obliged to provide medical insurance to all employees. The cost of insurance varies greatly depending on the tier of cover and the type and scope of cover provided, as well as the insurer. Insurance can start from as low as AED 800 per month and increase to as much as AED4,000 or more, depending on the plan and cover.
Gratuity is a payment accrued each month by employers to pay to an employee when they leave. Think of it as a pension accrual. Each month 10% of the basic salary is set aside and, if the employee leaves after 12 months of continued employment the accrued amount is paid to them. If they leave before 12 months, no payment is made. The % to accrue is approx. 6% of basic salary per month, for the first five years, but there are online calculators to help you calculate the right amount each month.
Consider using an Employer of Record (EoR) when you are starting out in UAE. The EoR is expert in this field and is a great solution for small companies who need to recruit but want to avoid the high costs and administration associated with hiring. As we illustrated before, the process to recruit is quite involved and can be time consuming as well as expensive and this is sometimes just not affordable for a small, new to region company.
For a monthly or fixed fee, the EoR will essentially take care of everything from the contract to the visa, patrol and medical insurance, leaving you free to manage the day-to-day relationship with the candidate. It can be a very cost-effective way to grow your team.
And finally……
Attestation of education certificates was not something we had come across before. However, we soon realised that many executive and professional roles need to provide an attested degree certificate to get an employment visa or employment card.
The attestation verifies that the qualification is legitimate, and that a person is qualified for the position that they are being hired for. The document also needs to be translated into Arabic which costs approx. AED 200 per page.
The cost for attestation can be as much as AED 2,000 and processing time varies 7 business days to 30+ business days, subject to the country where the qualification was gained. This can lead to lengthy delays in hiring and potentially lost business if you cannot put people on the ground because they don’t have a visa.
If a candidate is not yet in region, encourage them to have their educational certificates attested before travelling to UAE as it is much faster and cheaper than doing so from UAE. The total cost in the UK, as an illustration, will be between £150-£250 and involves, versus AED 2,200 from inside UAE. The process is very simple and involves;
1. A lawyer or notary public making a certified copy of the certificate(s) and adding their stamp
2. An Apostille from the foreign office. This can be done online in many countries, but will vary from country to country
3. UAE Embassy will verify the apostille and add their own approval seal
4. Finally, the MOFA must attest the document. This can be done in country when the applicant arrives either online (www.mofaic.gov.ae), sending the documents by courier or by visiting a Customer Happiness centre.
Artificial Intelligence (AI) is rapidly transforming how banks operate — from automating credit assessments and fraud detection to driving personalised customer engagement and compliance analytics. Yet, as its influence grows, regulators worldwide are intensifying scrutiny to ensure that AI-driven decision-making aligns with principles of transparency, accountability, and fairness. For banks, the regulatory implications extend across governance, risk management, model validation, data ethics, and operational resilience.
Supervisory authorities such as the European Central Bank (ECB), UK’s Prudential Regulation Authority (PRA), and Monetary Authority of Singapore (MAS) are clear: boards remain ultimately responsible for the safe and sound use of AI. This means that banks must embed AI within existing governance structures — ensuring senior management oversight, defined accountability lines, and board-level understanding of model behaviour and risks. Regulators expect banks to apply the same standards of internal control, auditability, and documentation to AI as they do to traditional financial models.
AI introduces model risk on a new scale. Machine-learning systems, particularly deep-learning models, can act as opaque “black boxes,” making it difficult to explain outcomes such as credit denials or transaction flagging. Regulators increasingly demand explainable AI (XAI): banks must demonstrate that models are interpretable, outcomes are traceable, and errors are correctable. The U.S. Federal Reserve’s SR 11-7 guidance on model risk management, already applied to traditional models, is being extended to AI contexts. European regulators, under EBA’s guidelines on loan origination and monitoring, similarly require justification of automated decisions affecting customers.
AI depends on data, often combining customer, transactional, and third-party sources. This creates friction with privacy frameworks such as GDPR and the UAE’s PDPL. Banks must ensure that AI systems respect data minimization, consent, and purpose-limitation principles. Regulators are increasingly assessing how synthetic data, data sharing, and LLM-based analytics comply with privacy laws. Any inadvertent exposure of personal or confidential information through AI systems may trigger supervisory actions and reputational damage.
AI models can unintentionally replicate or amplify societal biases. Regulators view algorithmic bias as both a conduct and prudential risk. Supervisors such as the EBA, FCA, and CFPB have issued guidance requiring banks to test for disparate impact, establish bias-mitigation controls, and maintain audit trails of data sources and model assumptions. Non-compliance could result in enforcement actions under consumer protection or equality laws.
AI introduces dependencies on third-party models, APIs, and cloud environments — increasing operational complexity and exposure to cyber threats. Under frameworks such as DORA (Digital Operational Resilience Act) in the EU and the CBUAE’s Operational Risk Regulation, banks must demonstrate resilience in AI systems, including continuity planning, incident response, and model-retraining procedures after disruption.
Globally, regulators are moving toward AI-specific governance frameworks. The EU’s AI Act, the UK’s AI Regulation Roadmap, and regional supervisory “sandboxes” set new precedents. The trend is clear: AI must be trustworthy, explainable, fair, and controllable. For banks, this requires a shift from ad-hoc innovation to regulated adoption, integrating AI oversight into enterprise risk frameworks, model committees, and compliance testing regimes.
The fintech sector in the UAE and the broader Gulf Cooperation Council (GCC) region is undergoing rapid growth, fueled by supportive regulatory frameworks, such as those from the Dubai International Financial Centre (DIFC) and Abu Dhabi Global Market (ADGM), and a regional push for digital transformation. Large financial institutions, including banks and insurance companies, are increasingly looking to fintechs to enhance operational efficiency, improve customer experiences, and stay competitive in a digital-first economy. However, adopting fintech solutions presents significant challenges for enterprises due to their complex operational structures, stringent regulatory requirements, and risk-averse cultures.
This white paper, developed in partnership between Finbridge Global (www.finbridgeglobal.com) and The Impact Team (www.theimpact.team), examines the key challenges large financial institutions face when implementing fintech solutions in the UAE and Gulf region. It highlights how the unique platform facilitates seamless adoption by accelerating partnership between fintechs and financial institutions at every stage of the adoption journey. The platform provides technical and regulatory support, fostering trusted partnerships to drive financial innovation.
Large financial institutions in the UAE operate within hierarchical structures, involving multiple stakeholders in procurement decisions. This complexity creates significant barriers to adopting fintech solutions.
Extended procurement processes delay innovation adoption, potentially causing enterprises to lag behind competitors and they do tend to kill the fintechs. The resource-intensive nature of due diligence and PoCs can strain budgets and divert focus from core operations.
Our partnership leverages Finbridge Global’s AI-powered platform to streamline procurement by connecting enterprises with pre-vetted fintechs, reducing the time spent identifying suitable vendors. The Impact Team provides consultancy expertise to align stakeholder priorities, facilitating faster consensus-building. Together, we offer curated PoC frameworks, ensuring efficient evaluations with clear success metrics.
The financial services sector in the UAE and GCC is tightly regulated, with compliance requirements posing significant challenges to fintech adoption.
Non-compliance risks regulatory penalties, reputational damage, and operational disruptions. The cost of validating fintech compliance can be substantial, particularly for multinational institutions navigating cross-border regulations.
Finbridge Global provides a single platform for fintech credentials and it does guide the fintech to what is needed to be ready to work with financial institutions. It does also provide access to regulatory guidance tailored to UAE and GCC markets, partnering with compliance experts to ensure fintech solutions meet enterprise standards. The Impact Team’s expertise in governance frameworks helps enterprises integrate compliant fintech solutions, reducing regulatory risks and ensuring alignment with local and international standards.
Enterprises prioritize stability and reliability, making trust a critical factor in fintech adoption.
Lack of trust can lead enterprises to favour established vendors, limiting access to innovative solutions. Security breaches or cultural mismatches can disrupt operations and erode customer confidence. This is not always the best customer outcome.
Finbridge Global curates a network of vetted fintechs with proven solutions, providing enterprises with detailed performance metrics and case studies to build trust. It does also force the fintech to maintain updated credentials in the platform to ensure compliance. The Impact Team fosters cultural alignment through workshops and change management strategies, ensuring effective collaboration. Our partnership also prioritizes cybersecurity, leveraging The Impact Team’s expertise to implement advanced protocols, safeguarding enterprise data.
Integrating fintech solutions into enterprise IT ecosystems is a major challenge due to reliance on legacy infrastructure.
Integration challenges can lead to prolonged implementation timelines, increased costs, and operational disruptions. Failure to address scalability or security concerns risks system failures and data breaches.
Finbridge Global provides technical specifications and integration roadmaps, connecting enterprises with fintechs optimized for legacy systems. At Finbridge global we don’t believe you need to be the best but the best match. The Impact Team’s digital transformation expertise ensures seamless integration, minimizing disruptions. We have established partnership discounts with integration specialists to address scalability and security, ensuring compliance with standards like ISO 27001.
Adopting fintech solutions requires significant enterprise resources, posing challenges for large institutions.
High costs and resource demands can delay fintech adoption, reducing competitive advantage. Inefficient vendor management risks partnership failures and missed innovation opportunities.
Our partnership reduces costs by streamlining vendor selection through Finbridge Global’s platform, which offers pre-vetted fintechs and clear evaluation metrics. From scouting to selecting to onboarding to monitoring. Finbridge Global streamlines the process end to end. The Impact Team provides governance frameworks to optimise vendor management, ensuring efficient resource allocation and sustained partnership success.
Enterprises and fintechs often have differing priorities, complicating adoption.
Misaligned expectations can result in failed partnerships or solutions that do not meet enterprise needs, wasting resources and delaying innovation.
Finbridge Global helps enterprises identify fintechs with aligned value propositions, using market insights to match solutions to specific needs. The Impact Team facilitates workshops to align strategic goals, ensuring fintechs meet enterprise expectations for customisation and long-term impact.
Large financial institutions in the UAE and GCC face significant challenges in adopting fintech solutions, from complex procurement and regulatory hurdles to trust gaps and technical integration issues. These barriers can delay innovation, increase costs, and limit competitive advantage. The partnership between Finbridge Global and The Impact Team addresses these challenges by providing a comprehensive ecosystem that connects enterprises with vetted fintechs, streamlines procurement, ensures regulatory compliance, and facilitates seamless integration.
By leveraging Finbridge Global’s AI-powered platform and The Impact Team’s digital transformation expertise, enterprises can overcome adoption barriers and unlock the full potential of fintech innovation. We invite financial institutions across the UAE and Gulf region to join our ecosystem at www.finbridgeglobal.com, where innovation meets opportunity, to shape the future of financial services.
Finbridge Global is the only AI powered platform that accelerates partnership at every stage of the adoption journey. Technology is moving so fast that you can no longer afford to sit and wait.
Says Finbridge Global CEO Barbara Gottardi;
“We don’t believe the process should re-start every time you change team, we don’t believe institutions should re-ask the same questions in a different format and we know for sure that no financial institution is so different in what they are asking.
We also know that fintech should spend most of their time in building a resilient product and ensuring all certifications are constantly updated. Copying and pasting information in different spreadsheets or forms is not an added-value task”
“We have worked in the industry and we have built this with the industry”
Finbridge Global is a platform designed to bridge the gap between fintechs and enterprise clients. By offering a curated network, regulatory guidance, technical support, and market insights, they enable fintechs to successfully sell their solutions to banks and financial institutions while helping enterprises evaluate and adopt innovative technologies. Visit www.finbridgeglobal.com to learn more and join their mission to drive financial innovation.
The Impact Team is a European and UAE digital transformation consultancy that partners with organisations to enhance their digital products and services. Their expertise encompasses advising on team structures, managing design operations, and implementing governance frameworks, all with a focus on customer-centric solutions and effective execution.
Recognising the importance of continuous improvement, The Impact Team integrates change within organisations to swiftly respond to evolving market demands. They foster a culture of innovation and adaptability, embedding these principles into the organisational fabric.
In the realm of cybersecurity, they employ advanced technologies and best practices to protect data, systems, and networks from malicious attacks and vulnerabilities. This approach ensures that digital assets remain secure and resilient against evolving cyber risks.
The Impact Team operates globally, with offices in London, New York, Hong Kong and Dubai, enabling them to deliver tailored digital transformation services across various regions.
Their mission is to empower organisations to thrive in the digital age while fostering a sustainable and responsible future. They are committed to providing ESG-friendly solutions that drive meaningful change and create value for clients, society, and the planet.
Through their comprehensive approach, The Impact Team aims to transform businesses by fine-tuning operations to achieve tangible, impactful results, ultimately contributing to business growth and success.
Want to get in touch? Reach out at contactme@theimpact.ae
The fintech industry in the UAE and globally is experiencing unprecedented growth, driven by rapid digital transformation, increasing demand for innovative financial solutions, and supportive regulatory frameworks such as those provided by the Dubai International Financial Centre (DIFC) and Abu Dhabi Global Market (ADGM). Fintech companies are developing cutting-edge solutions ranging from payment processing and blockchain-based platforms to artificial intelligence-driven analytics and regtech tools. However, despite their innovative offerings, fintechs face significant challenges when attempting to sell their products to large enterprise clients, such as banks and financial institutions. These challenges stem from structural, operational, cultural, and regulatory differences between nimble fintech startups and established enterprises.
This white paper explores the key barriers fintechs encounter when engaging with large enterprise clients and highlights how Finbridge Global (www.finbridgeglobal.com) addresses these challenges by connecting fintechs with enterprise clients, facilitating smoother partnerships and fostering innovation in the financial services ecosystem.
One of the most significant hurdles fintechs face when selling to large enterprise clients is the prolonged and complex sales cycle. Unlike smaller businesses or direct-to-consumer models, enterprise sales, particularly in the banking sector, involve multiple stakeholders, rigorous due diligence, and extended decision-making processes.
Impact on Fintechs
The extended sales cycle can be particularly challenging for fintech startups, which often operate with limited cash flow and lean teams. Prolonged negotiations and delayed revenue generation can hinder growth and divert focus from product development and innovation.
Finbridge Global’s Solution
Finbridge Global streamlines the sales process by acting as a trusted intermediary. The platform connects fintechs with pre-vetted enterprise clients, reducing the time spent identifying and engaging decision-makers. By providing a centralized hub for showcasing fintech solutions, it enables enterprises to evaluate products efficiently, shortening the sales cycle and accelerating partnerships.
The financial services industry is one of the most heavily regulated sectors globally, and the UAE is no exception. Fintechs must navigate a complex web of regulations, including anti-money laundering (AML), know-your-customer (KYC), data protection (e.g., UAE’s Federal Decree-Law No. 45/2021 on Personal Data Protection), and sector-specific guidelines from regulators like the Central Bank of the UAE and the Securities and Commodities Authority.
Impact on Fintechs
Failure to meet regulatory requirements can result in lost opportunities or reputational damage. The cost of building compliant systems or hiring legal and compliance experts can be prohibitive for early-stage fintechs.
Finbridge Global’s Solution
Finbridge Global provides fintechs with access to regulatory guidance and resources tailored to the UAE and global markets. The platform partners with certified experts to help fintechs align their offerings with enterprise expectations, ensuring smoother onboarding and reducing regulatory friction.
Large enterprises, particularly banks, prioritize stability and reliability when selecting technology partners. Fintech startups, often perceived as unproven or risky, struggle to establish trust and credibility.
Impact on Fintechs
The lack of trust and credibility can lead to missed opportunities, as enterprises opt for established vendors over innovative but unproven fintechs. This creates a barrier to market entry, particularly for early-stage companies.
Finbridge Global’s Solution
Finbridge Global bridges the trust gap by curating a network of vetted fintechs with proven solutions. The platform provides enterprises with detailed profiles, case studies, and performance metrics, enabling informed decision-making. Additionally, the team facilitates introductions and fosters alignment between fintechs and enterprises, ensuring cultural compatibility and mutual understanding.
The initial assessment does provide an objective score on the maturity of the fintech so you can quickly see if it is a good match for your organisation
Integrating fintech solutions into the complex IT ecosystems of large enterprises is a significant hurdle. Banks often rely on legacy systems, which are not always compatible with modern fintech platforms.
Impact on Fintechs
Technical integration challenges can lead to prolonged implementation timelines or outright rejection of fintech solutions. The cost of customizing solutions to fit legacy systems can strain fintech resources, while failure to meet security standards can erode trust.Fintechs tends to prioritize a quick MVP but not building secure from the beginning and with scalability in mind is a costly mistake
Finbridge Global’s Solution
Finbridge Global facilitates technical alignment by providing enterprises with detailed technical specifications and integration roadmaps for fintech solutions. The platform connects fintechs with integration specialists who can assist in navigating legacy systems and ensuring compliance with security standards, enabling seamless adoption.
A partnership with Drata allows fintech to receive a very discounted ISO certification together with more valuable ones
Fintech startups often operate with limited resources, making it difficult to compete with established vendors for enterprise contracts.
Impact on Fintechs
Resource constraints can prevent fintechs from effectively competing in the enterprise market, limiting their growth potential and market share.
Finbridge Global’s Solution
Finbridge Global levels the playing field by providing fintechs with access to a targeted network of enterprise clients in the UAE and beyond. The platform reduces the cost of client acquisition by facilitating direct connections and providing marketing support, enabling fintechs to focus on innovation rather than resource-intensive sales efforts.
Members can also benefit from marketing, legal & insurance advice from their partners
Fintechs and enterprises often have misaligned expectations regarding the value and implementation of fintech solutions.
Impact on Fintechs
Misaligned expectations can result in failed partnerships or dissatisfaction, as enterprises feel that fintech solutions do not fully meet their needs.
Finbridge Global’s Solution
Finbridge Global helps fintechs refine their value propositions to align with enterprise priorities. The platform provides market insights and facilitates workshops to ensure fintechs understand and address enterprise needs, fostering mutually beneficial partnerships.
The fintech industry holds immense potential to transform financial services, but selling to large enterprise clients remains a formidable challenge. From navigating complex sales cycles and regulatory requirements to overcoming trust gaps and technical integration hurdles, fintechs face a myriad of obstacles that can hinder their success. These challenges are particularly pronounced in the UAE, where the financial sector is both highly competitive and tightly regulated.
Finbridge Global, launched at www.finbridgeglobal.com, is uniquely positioned to address these challenges. By connecting fintechs with enterprise clients, providing regulatory and technical support, and facilitating trust-building, the platform empowers fintechs to overcome barriers and deliver value to large enterprises. Finbridge Global is the only AI powered platform that accelerates partnership at every stage of the adoption journey
Says Finbridge Global CEO Barbara Gottardi “We don’t believe the process should re-start every time you change team, we don’t believe institutions should re-ask the same questions in a different format and we know for sure that no financial institution is so different in what they are asking.
We also know that fintech should spend most of their time in building a resilient product and ensuring all certifications are constantly updated. Copy and pasting information in different spreadsheet is not an added-value task
We have worked in the industry and we have built this with the industry”
By inviting fintechs and financial institutions in the UAE and beyond to join the ecosystem, where innovation meets opportunity, they are shaping the future of financial services.
About Finbridge Global
Finbridge Global is a platform designed to bridge the gap between fintechs and enterprise clients. By offering a curated network, regulatory guidance, technical support, and market insights, they enable fintechs to successfully sell their solutions to banks and financial institutions while helping enterprises evaluate and adopt innovative technologies. Visit www.finbridgeglobal.com to learn more and join their mission to drive financial innovation.
About The Impact Team
The Impact Team is a European and UAE digital transformation consultancy that partners with organisations to enhance their digital products and services. Their expertise encompasses advising on team structures, managing design operations, and implementing governance frameworks, all with a focus on customer-centric solutions and effective execution.
Recognising the importance of continuous improvement, The Impact Team integrates change within organisations to swiftly respond to evolving market demands. They foster a culture of innovation and adaptability, embedding these principles into the organisational fabric.
In the realm of cybersecurity, they employ advanced technologies and best practices to protect data, systems, and networks from malicious attacks and vulnerabilities. This approach ensures that digital assets remain secure and resilient against evolving cyber risks.
The Impact Team operates globally, with offices in London, New York, Hong Kong and Dubai, enabling them to deliver tailored digital transformation services across various regions.
Their mission is to empower organisations to thrive in the digital age while fostering a sustainable and responsible future. They are committed to providing ESG-friendly solutions that drive meaningful change and create value for clients, society, and the planet.
Through their comprehensive approach, The Impact Team aims to transform businesses by fine-tuning operations to achieve tangible, impactful results, ultimately contributing to business growth and success.
contactme@theimpact.ae
Shadow AI—the unauthorized use of generative AI tools such as ChatGPT, Claude, or Gemini—poses a growing threat to highly regulated industries. Unlike Shadow IT, it does not leave behind files or logs within enterprise systems. Instead, it silently exfiltrates sensitive data into external AI platforms, leaving compliance teams blind.
Financial services and healthcare organizations must respond now. Without controls, Shadow AI risks breaches of GDPR, HIPAA, FCA, and other mandates. This paper explores the nature of the risk, why traditional safeguards fail, and the steps required to restore visibility and governance.
contactme@theimpact.ae
The term Shadow IT traditionally referred to the unauthorized use of software-as-a-service (SaaS) applications or cloud-based tools that had not been formally approved by the organization’s IT department. While this behavior introduced risks—including data sprawl, inconsistent access controls, and potential regulatory violations—it was at least detectable. Unauthorized applications typically generated residual evidence in the form of login attempts, cloud storage folders, browser histories, or email correspondence. Compliance teams could, with the right effort, trace activity, audit logs, and reconstruct what data had been exposed. Shadow IT, while challenging, was not invisible.
Shadow AI, by contrast, is significantly more insidious. When an employee copies sensitive information—such as financial projections, patient records, or intellectual property—into a browser-based generative AI tool, there is no locally stored file, email attachment, or system log to review. The interaction exists only as a prompt sent to an external service provider, typically over an encrypted connection. This bypasses traditional detection methods, rendering the activity invisible to Security Information and Event Management (SIEM) platforms, Data Loss Prevention (DLP) systems, and even the most rigorous compliance audits.
The enterprise, therefore, loses both visibility—the ability to monitor or detect the activity—and control—the ability to enforce policy, retract data, or remediate exposure once the information has been transmitted. Unlike Shadow IT, which at least left behind a forensic trail, Shadow AI operates in complete darkness, making it not just another iteration of unauthorized technology use, but an entirely new category of governance challenge.
Shadow AI rarely begins as a deliberate act of negligence. More often, it grows from a well-intentioned pursuit of efficiency. Employees under pressure to deliver faster results or manage heavy workloads may turn to readily available generative AI tools as “assistants.” Unlike traditional software procurement, which requires IT approval and integration, browser-based AI tools are frictionless: they require no installation, no contract, and no oversight. A simple copy-and-paste is all it takes.
Consider a financial analyst working on a high-stakes client pitch. Faced with the need to summarize hundreds of lines of financial models into a concise executive slide, the analyst turns to ChatGPT. With a few keystrokes, sensitive client data leaves the safety of the enterprise environment and enters an external large language model.
Or take a hospital researcher drafting a clinical letter. Instead of manually formatting and writing the correspondence, the researcher enters real patient information into an AI platform to save valuable time. While the intent is productivity, the outcome is uncontrolled data exfiltration.
The critical issue is that once information enters a generative AI system:
· It is Untraceable – No audit trail exists within the enterprise. Unlike emails, file transfers, or database queries, prompt inputs are not captured by existing monitoring systems. Compliance officers cannot reconstruct what was shared, when, or by whom.
· It is Irretrievable – Even if an AI provider pledges not to retain inputs, there is no practical mechanism to retract or delete what has already been transmitted. In non-enterprise versions, prompts may be used transiently in model training or optimisation, creating additional uncertainty.
· It is Non-compliant – Sensitive information such as Personally Identifiable Information (PII), Protected Health Information (PHI), or regulated financial data may be processed outside the boundaries of GDPR, HIPAA, or industry-specific mandates. The mere act of transmission can constitute a breach, regardless of whether the data is later stored or used.
In short, Shadow AI does not require malicious actors or intentional policy violations to occur. It emerges organically, as employees normalize the use of external AI platforms to accelerate tasks. This very normalization makes the phenomenon both pervasive and dangerous: it is invisible, ungoverned, and almost always underestimated.
Traditional governance frameworks often operate under the assumption that written policies, codes of conduct, and acceptable-use agreements are sufficient to mitigate risk. Employees are expected to read, acknowledge, and adhere to these policies, while managers and compliance officers rely on the idea that documented rules equal protection. In practice, however, these mechanisms are inadequate in the face of Shadow AI. A policy without enforcement is, at best, aspirational. At worst, it provides a false sense of security.
The shortcomings become clear when critical questions are posed:
· Can the organization identify which employees are actively using ChatGPT, Gemini, or other generative AI platforms? Most monitoring systems do not capture such usage, particularly when accessed through encrypted web sessions.
· Can the organization log the specific prompts or data inputs being entered? Unlike emails or file transfers, prompts do not leave behind auditable records within corporate systems. Without this visibility, compliance teams cannot assess the scope of exposure.
· Can the organization prevent an employee from copying and pasting sensitive data—such as PHI, PII, or financial disclosures—into an external AI tool? For the majority of firms, there are no technical guardrails in place to block such actions.
For most enterprises, the answer to all three questions is unequivocally “no.”
This blind spot represents more than just a gap in oversight—it is a fundamental governance failure. Traditional data protection solutions, including SIEM, DLP, and firewall technologies, were designed to monitor structured events like file transfers, email attachments, or network traffic. They were not built to analyze freeform, prompt-based interactions between employees and AI platforms. As a result, compliance officers cannot see what data leaves the organisation, cannot quantify the risk, and cannot demonstrate adherence to regulatory mandates.
In effect, Shadow AI has rendered legacy governance models obsolete. Organizations may believe they are compliant on paper, yet in practice, they are operating in an environment where sensitive data can leak undetected every day.
Employees frequently view AI assistants as harmless, everyday productivity enhancers. Unlike phishing attempts, ransomware, or malware intrusions, generative AI tools do not trigger alarms or raise suspicion. Instead, they present themselves as helpful, intuitive, and user-friendly companions. This perception is precisely what lowers vigilance: because employees believe they are simply “getting a little help,” they rarely pause to consider the compliance, privacy, or security consequences of their actions.
The normalization of Shadow AI is reinforced by organizational culture itself. Many workplaces reward speed, efficiency, and innovation, often under tight deadlines and with mounting workloads. In this environment, employees who find faster ways to complete tasks—whether preparing reports, summarizing data, or drafting communications—are praised for their initiative. Generative AI seamlessly fits into this narrative, positioning itself as a shortcut to productivity rather than a source of risk.
Yet the dangers are profound. When a financial controller pastes draft earnings figures into ChatGPT to refine the tone of a quarterly report, that act may inadvertently constitute premature disclosure of market-sensitive information. Similarly, when a healthcare administrator drafts a patient discharge letter using an AI platform, protected health information (PHI) may be exposed to an external system outside the scope of regulatory compliance. Neither employee intended harm; both believed they were being efficient.
The cultural framing of generative AI as “just a tool” masks its true nature: it is a channel of data exfiltration operating in plain sight. Unlike malicious external threats, which feel dangerous and invite suspicion, Shadow AI feels benign and familiar. This illusion of safety is what makes it particularly insidious. By the time compliance officers become aware of its use, sensitive data may already have been processed, replicated, or incorporated into models beyond the enterprise’s reach.
In short, Shadow AI thrives because it feels normal—and in modern workplaces, what feels normal is rarely questioned. Unless organizations actively challenge this cultural acceptance, the quiet adoption of generative AI will continue to erode the very foundations of data governance and regulatory compliance.
Shadow AI cannot realistically be eradicated. Employees will continue to experiment with generative AI tools, driven by the promise of speed and efficiency. However, its risks can be managed through a coordinated strategy that blends technology, governance, and culture. Four key actions stand out:
Together, these four measures transform Shadow AI from an ungoverned, invisible risk into a managed domain of enterprise technology. The objective is not to suppress innovation, but to channel it safely—ensuring that employees can leverage the power of generative AI without undermining regulatory obligations, client trust, or organizational resilience.
Shadow AI is the evolution of Shadow IT—subtler, harder to detect, and capable of causing significant regulatory harm. Financial services and healthcare organizations must act immediately to establish governance and restore visibility.
The Impact Team partners with enterprises to deliver safe adoption pathways, visibility, and governance frameworks for AI. To discuss how we can help protect your organization, contact us today.
contactme@theimpact.ae
Transformation in financial institutions isn’t about technology — it’s about trust, timing, and truth.
Across the Gulf, Europe, and Africa, banks and insurers are investing billions in digital modernisation: core-banking replacements, AI governance, cloud adoption, and compliance automation. Yet most transformations still underperform not because of poor strategy, but because of human dynamics: unclear intent, cultural inertia, or misaligned incentives.
At The Impact Team, we’ve delivered and rescued dozens of large-scale transformations. From that experience, we’ve distilled twenty enduring truths — each one a recurring pattern in the lived reality of change inside a regulated financial institution.
What follows is not a framework, but a field manual — drawn from boardrooms, transformation offices, and war rooms — about what really determines whether transformation endures or unravels.
When projects falter, leaders instinctively tighten control: more steering committees, more sign-offs, more slide decks. Yet real progress rarely comes from command; it comes from clarity.
One Middle Eastern bank replaced six layers of programme governance with a single weekly “clarity session.” Each team articulated why their work mattered — not just what they were doing. Within three months, duplication fell by 40%.
Control limits risk; clarity releases energy. When everyone understands the destination and the non-negotiables, decision-making becomes distributed without losing coherence. Clarity transforms compliance into conviction.
Transformation exposes leadership fragility. Many executives sponsor change until it threatens their comfort zones — power, process, or prestige.
At one European retail bank, the CEO launched an “Agile Everywhere” campaign but insisted on personally approving every resource request above €10,000. Agility died on contact with hierarchy.
True transformation requires leaders to model discomfort — to dismantle their own bottlenecks first. Courage is contagious: when the top is seen to stretch, the organisation follows.
Comfort is the enemy of credibility; transformation demands leaders who can hold uncertainty publicly.
Culture is not shaped by mission statements — it’s defined by what leaders walk past.
In one Gulf insurer, late delivery and hidden defects became normal because executives never challenged them. By contrast, a rival institution introduced “leadership audits” — monthly reviews not of KPIs, but of cultural consistency. Within a year, escalation and ownership improved dramatically.
Transformation requires visible intolerance for behaviours that erode trust: passive resistance, political interference, or avoidance of accountability. The culture you tolerate today becomes the operating model you inherit tomorrow.
Pilots are mirrors of intent. A truly innovative strategy funds experiments with real customers and measurable risk; a defensive one funds PowerPoint.
When a large bank’s AI pilot was forced to use synthetic data to avoid audit concerns, it revealed more about leadership’s fear of exposure than its appetite for innovation.
The design of a pilot — who owns it, what risk it takes, how success is defined — exposes the institution’s real priorities. If every pilot is safe, your strategy is performative.
Pilots should be laboratories of learning, not museums of control.
Resistance is the market research you didn’t pay for.
When front-office teams resist a new onboarding workflow, they’re revealing what doesn’t fit the real world. Dismissing their concerns as “old-school” loses insight; decoding their pushback reveals friction you need to fix.
At one UAE bank, transformation leaders created “resistance roundtables” — 30-minute open sessions where staff could air frustrations directly. The outcomes became design inputs. Resistance turned into participation.
Change fails when leaders suppress dissent. Listening deeply to resistance turns it from obstruction into acceleration.
Transformation programmes often prioritise messaging over meaning. Weekly newsletters, town halls, and glossy dashboards proclaim progress — yet delivery metrics quietly shift.
When words and actions diverge, belief collapses. A leading regional bank lost its top digital engineers after the third “agile transformation” announcement without actual backlog reprioritisation.
Consistency is the real language of leadership. Teams forgive delays; they don’t forgive hypocrisy. Trust compounds when communication aligns with lived reality.
No system upgrade can fix a culture of concealment.
Banks often talk about “single sources of truth,” yet fear of reputational risk drives data sanitisation. In one European bank, critical incident data was routinely downgraded to avoid executive confrontation. The data warehouse became a monument to self-censorship.
An honest data culture encourages surfacing ugly truths early. Transparency must be rewarded, not punished. Data integrity is a cultural outcome, not a technical one.
Employees don’t resist transformation because they hate innovation; they fear what it might take from them — status, control, identity, or job security.
At a GCC bank migrating to cloud infrastructure, operations teams resisted automation scripts until leadership reframed the shift: from “reducing manual work” to “freeing capacity for higher-value security monitoring.” The narrative changed everything.
Leaders must acknowledge loss honestly, then replace it with purpose. When people understand what they gain, fear turns into ownership.
Dashboards deliver compliance, not conviction.
Transformation programmes that motivate purely through metrics — reduced cycle time, improved accuracy, lower OPEX — often achieve process change but not emotional engagement.
Meaning comes from connection: why the change matters. When a financial-crime compliance team learned that automation reduced false-positive investigations, freeing time to detect real threats, their engagement soared.
People don’t fight for percentages. They fight for purpose.
Executives set direction, but middle managers set momentum.
They translate strategy into reality, control resource allocation, and define what “priority” actually means day-to-day. Yet they’re often the most neglected audience in transformation.
A core-banking replacement in a North African bank floundered until middle managers were integrated into sprint reviews and empowered to make backlog decisions. Suddenly, dependencies cleared.
Ignore this layer and change will stall in bureaucracy. Empower it and transformation accelerates naturally.
Harmony feels safe — but in complex, regulated environments, it’s often a symptom of fear.
When steering committees display only “green” traffic lights, it means honesty has been replaced by performance theatre. At one insurer, every project reported on target until the regulator arrived — and discovered half the documentation missing.
Psychological safety allows truth to surface early. Transformation requires courageous conflict — because disagreement is data, not disruption.
Transformation success is rarely captured by budget, timeline, or compliance metrics.
A digital initiative that delivers on time but fails to change behaviour isn’t transformation — it’s project completion. The real question: are customers acting differently? Are employees making better decisions faster?
Progress must be measured in adoption, satisfaction, and sustainability — not just delivery. What gets measured gets managed; what gets lived gets transformed.
Declaring “this is urgent” is easy. Providing a clear, prioritised path is leadership.
A global bank’s “digital urgency” campaign led to 37 parallel initiatives — all competing for the same funding and attention. Within a year, fatigue replaced momentum.
Urgency motivates only when accompanied by focus. Direction turns urgency into energy; without it, belief burns out long before the strategy delivers.
Governance is the spine of transformation — it holds flexibility upright.
Banks often confuse bureaucracy with governance. True governance protects velocity by providing clarity: who decides, who signs off, and who escalates.
A regional regulator praised one institution’s transformation because every major decision had a visible risk owner and a traceable rationale. That visibility built confidence with auditors and freed delivery teams to move faster.
Governance done right is not paperwork; it’s protection.
In many transformation meetings, the people who speak the most contribute the least insight.
Front-line employees often see problems first — but hierarchy muffles them. One bank created an “idea dividend” system, rewarding insights from any grade that led to measurable improvement. The majority of breakthroughs came from staff two levels below management.
Leadership listening must be tuned for signal, not volume. The quietest observations often contain the highest truth density.
The glamorous narrative of innovation — AI, digital twins, blockchain — hides the dull, disciplined labour underneath: data mapping, policy harmonisation, identity clean-up.
A Gulf bank’s AI programme spent its first six months cleaning metadata. The executives grew restless — until the first model trained flawlessly.
Transformation is unglamorous until the compounding effort clicks. The boring work is the brilliance — just not yet visible.
Fearful environments breed silence; silence kills innovation.
When senior leaders treat every red flag as failure, employees learn to hide risk. One bank’s CIO reversed this by instituting a “Friday Failure Forum” — open discussions of what went wrong and what was learned. Within a quarter, escalation times halved.
Psychological safety isn’t about comfort; it’s about courage. Leaders must show vulnerability first if they want truth to surface.
Go-live is the start, not the finish line.
Adoption happens incrementally — when users discover daily that the new way works better. In a credit-card operations team, adoption of a new case-management system plateaued until managers began celebrating “small wins” weekly. Engagement surged.
Sustained transformation requires ongoing reinforcement — communication, coaching, and iteration. Adoption isn’t mandated; it’s maintained.
When communication dries up, it’s not calm — it’s disengagement.
During a data-governance rollout, feedback channels went quiet. Leadership assumed success until a whistle-blower revealed teams had stopped using the tool altogether. Silence had been misread as alignment.
Leaders must treat silence as a signal: re-engage, re-explain, or re-inspire. Transformation dies not with protest, but with apathy.
The visible side of transformation — roadmaps, KPIs, dashboards — fades. What remains are habits: documentation discipline, risk awareness, continuous learning.
In one regulator’s innovation unit, a single habit — publishing weekly “lessons learned” memos — outlasted three reorganisations and became part of institutional DNA.
Legacy is not declared; it’s repeated. The quiet rituals of responsibility are what make transformation permanent.
Transformation within financial institutions is rarely a story of technology; it’s a story of behaviour.
Each of these twenty truths points to the same lesson: execution is emotional. Clarity, courage, and consistency matter more than any framework.
Institutions that master these human dimensions don’t just deliver digital projects — they evolve their identity. They move from compliance to confidence, from control to clarity, and from change fatigue to change fluency.
At The Impact Team, we believe transformation is not an event but a lived experience — one that demands integrity, rhythm, and relentless learning.
The Impact Team partners with financial institutions and regulators across the Gulf, Europe, and Africa to deliver measurable transformation. We specialise in digital modernisation, AI governance, cyber resilience, and regulatory technology — helping our clients move from strategy to execution with speed, safety, and certainty.
The Impact Team
Accelerating Digital Execution. Securing Tomorrow’s Banks.
www.theimpact.ae
