AI, Risk & Trust

What Working in Banking Taught Me About AI, Risk, and Trust

Published: June 2026

When I first started working in the financial sector, I was surprised by how many important decisions were already being influenced by intelligent systems. At the time, I simply saw them as useful tools and technological improvements. Over time, I became increasingly curious about how artificial intelligence was quietly changing the way financial institutions identify risks, make decisions, and serve customers.

I still remember being struck by how quickly certain decisions in banking were being made. Fraud alerts appeared almost instantly, unusual transactions were flagged before customers noticed them, and risk assessments that once required significant manual effort were happening quietly in the background.

The more I explored that question, the more I realised that banking has quietly become one of the world's most active adopters of artificial intelligence. From fraud detection and credit scoring to regulatory compliance and transaction monitoring, AI is no longer an experiment in many institutions. It is becoming part of the infrastructure. Financial organisations are using tools such as machine learning models, robotic process automation, behavioural biometrics, natural language processing, and AI-enhanced risk management systems to support critical functions across their operations.

What stood out to me was not simply the scale of adoption.

It was the shift in mindset.

Traditionally, risk management was often reactive. Something happened, and the organisation investigated it. A suspicious transaction was reviewed after it occurred. A compliance issue was identified after an audit. A customer defaulted before warning signs were fully understood.

AI is changing that dynamic.

Instead of asking, "What went wrong?" organisations are increasingly asking, "What is likely to go wrong next?"

That difference is profound.

Imagine driving a car that only tells you about a hazard after you've already hit it. Now imagine a vehicle that continuously scans the road ahead and warns you before the danger appears. That is essentially the transition taking place in risk management.

One thing I kept coming back to throughout my research was the growing confidence financial professionals have in data-driven systems. Many now trust machine learning tools to process vast amounts of information more quickly and consistently than traditional manual approaches. AI systems can analyse transaction patterns, flag unusual activity, support compliance checks, and identify emerging risks in real time. What once required large teams and lengthy review processes can now happen continuously in the background.

Fraud detection is perhaps one of the most visible examples. A human analyst might struggle to review millions of transactions. An AI system can monitor those transactions simultaneously, looking for subtle behavioural patterns that may indicate something unusual. The result is not just speed. It is the ability to recognise signals that might otherwise be missed.

The same applies to regulatory compliance. Tasks such as Know Your Customer (KYC) and Anti-Money Laundering (AML) checks are increasingly being supported by AI-enabled systems that help institutions manage regulatory obligations with greater efficiency and consistency. [

But perhaps the most important lesson I learned is that technological capability is not the same thing as good judgement.

Just because an AI system can make a decision doesn't mean it should make that decision alone.

That may be where the real conversation begins.

The benefits of AI are easy to see. The challenges are often less visible.

Many AI systems learn from historical data. If historical decisions contain bias, those biases can find their way into future recommendations. A lending model trained on imperfect data may unintentionally disadvantage certain groups. A risk model may produce outcomes that appear objective while silently reflecting decisions made years earlier. Concerns around algorithmic bias, explainability, accountability, and privacy remain very real challenges for financial institutions.

The issue is not that AI makes mistakes.

Humans make mistakes too.

The issue is understanding how decisions are being made and who remains accountable when things go wrong.

I often think about this through the lens of trust.

When a customer applies for credit and receives a decision, should they be able to understand why that decision was made?

I believe they should.

When a transaction is flagged as suspicious, should there be a clear path for human review?

Again, I believe there should.

Technology should strengthen trust, not replace it.

The most effective financial institutions should not only be those that simply automate the most processes. They should be the ones that combine technological innovation with transparency, governance and human oversight. AI can help identify risks, but people must still define values, exercise judgement, and take responsibility for outcomes.

As I reflect on the future of banking, I am optimistic. AI has enormous potential to improve risk management, strengthen compliance, enhance fraud prevention, and support better decision-making. Yet the future of financial services will depend not only on how intelligent our systems become, but also on how responsibly we choose to use them.

The future of banking will not be shaped by how much AI we adopt, but by how wisely we choose to use it. And that leaves me with a question I continue to reflect on: as artificial intelligence becomes more intelligent, are we becoming more thoughtful about the responsibility that comes with it?

About the Author

Dr Iyanu Emmanuel Olatunbosun is an interdisciplinary researcher specialising in Responsible AI, FinTech innovation, digital financial inclusion, and sustainable digital transformation.

Related Research (Published 2025)

Olatunbosun, I. E., & Olatunbosun, A. R. (2025). Artificial Intelligence and Risk Management in Financial Institutions: Evidence from the UK Banking Sector. EthAIca, 4, 436. https://doi.org/10.56294/ai2025436