ADMIN ST BUSINESS 
Artificial intelligence is no longer some futuristic concept lurking in the wings; it’s right here, deeply woven into the financial sector. The Bank of England, a body we all look to for a steady hand, is trying to figure out just how this massive technological shift affects financial stability. It’s a genuine balancing act, seeing the huge productivity benefits AI offers while also grappling with some pretty stark new risks. The two are inextricably linked.
The central bank’s view seems to be that we’re in a phase of fast-paced change. They’ve noted that a significant percentage of firms are already using AI, and many more are planning to jump in soon. In fact, a recent joint survey by the Bank of England and the Financial Conduct Authority (FCA) found that 75% of firms are now using AI. This is great for efficiency, sure—think faster customer interactions and better fraud detection—but it also means AI is moving into the core financial decision-making of banks and insurers.
Leveraging AI for Risk and Efficiency (The Lottoland Example)
Look at a company like Lottoland, for instance. Its business model involves insuring massive jackpot payouts, but it is not the official lottery operator. Instead, it acts as a bookmaker that allows players to bet on the outcome of official draws. This is a prime example of where AI is indispensable for risk management. They aren’t just spinning a wheel; they’re constantly calculating and underwriting massive financial exposure.
- Risk Underwriting: They use AI for incredibly complex risk modelling and dynamic pricing—essentially, actuarial science on steroids—to calculate the real-time financial exposure of gigantic jackpots. This is necessary for dealing with such volatile, high-stakes outcomes.
- Compliance and Fraud: As a regulated online platform, they lean heavily on AI for Know Your Customer (KYC) checks and fraud detection, even using AI-powered identity verification. This cuts down on manual work, speeds up processes, and closes the door on fraudulent activity.
This illustrates the opportunity the Bank of England sees: using AI for risk modelling and data analysis leads directly to greater efficiency, better risk assessment, and stronger compliance. This is where things get interesting, and maybe a little worrying.
The Systemic Risk Problem
What exactly is the big worry? Well, it boils down to the potential for systemic risk. If a lot of different firms rely on similar AI models, what happens if that model has a fundamental, common weakness? If many institutions misjudge risk, or misprice credit, at the exact same time due to a flawed algorithm, you could see losses spread quickly across the whole system. Doesn’t that sound a bit like the collective mispricing of risk that fuelled the 2008 financial crisis? It certainly gives us pause for thought.
The Bank is also deeply focused on how AI affects financial markets. Increased use of advanced AI for trading, especially those systems that can act with more autonomy, could make market movements more correlated. If everyone’s using similar tech to inform their trading, a shock could be amplified far more quickly than it would be otherwise. We’re talking about AI potentially identifying and exploiting weaknesses in other firms, perhaps even learning that volatility is profitable for them and acting to increase it. It’s a wild thought, a bit unsettling, this idea of algorithms causing a crisis for profit.
The Path Ahead
The Bank of England is not halting AI adoption, but is closely monitoring whether existing technology-agnostic regulations are adequate. Due to AI’s unique ability to learn autonomously and generate unexplainable outcomes, the Bank is focused on evolving oversight to ensure managers understand and can govern their models, thereby supporting the safe integration of this powerful technology into the economy.
What do you think is the biggest systemic risk posed by widely adopted, autonomous AI models in the City?
