Fraud is now one of the most common crimes in the UK, and the businesses on the receiving end of it are under growing pressure to do more. Banks, insurers, retailers and online platforms all handle huge volumes of transactions every day, and no team of analysts can review them by hand. Increasingly, the first line of defence is software.
The approach is spreading across regulated sectors, from high-street banks and insurers to online casinos and betting operators, which have to verify customers, monitor accounts and report suspicious activity under strict licence conditions. Here is how the technology works, where it helps and what businesses need to watch.
What AI Does Well in Fraud Detection
Most fraud systems use machine learning to build a picture of what normal activity looks like, then flag anything that doesn’t fit. Typical signals include:
- Unusual transactions. A payment far larger than a customer’s usual pattern, or a burst of small payments in a short time.
- Location and device changes. A login from a new country or a device that has been linked to previous fraud.
- Behavioural patterns. Typing speed, navigation habits and other signs that a human is not the one using the account.
- Connected accounts. Networks of accounts sharing details, addresses or payment methods.
Unlike simple rules (“block any payment over £5,000”), machine learning can weigh hundreds of signals at once and adapt as criminals change tactics.
Verifying Who Customers Are
Identity checks are another major use. Many businesses now use automated tools to read an ID document, check that it’s genuine and compare a selfie or short video with the photo, a process often called liveness detection. Done well, it makes onboarding faster for honest customers and harder for people using stolen or fake identities.
For age-restricted products, the same technology helps prevent underage access. Businesses selling alcohol, vaping products or online gambling are all required to confirm age, and automated checks against databases or documents have become the standard way to do it.
Monitoring for Money Laundering
Anti-money-laundering rules oblige many sectors to monitor transactions and report suspicious activity. AI can screen payments against sanctions and watch lists, spot unusual patterns of deposits and withdrawals and prioritise alerts for human investigators.
This matters because traditional monitoring systems generate large numbers of false alarms. Better models can reduce that noise, so compliance teams spend their time on genuine concerns instead of working through endless harmless alerts.
Protecting Customers From Harm
Fraud isn’t the only risk. In gambling, the Gambling Commission’s licence conditions require remote operators to have effective systems for monitoring customer activity to identify harm from the point an account is opened, using a range of indicators that includes customer spend. The Commission’s guidance adds that taking action in a timely way will in some cases mean automated, real-time measures, which is only practical with software doing the monitoring.
Similar duties exist elsewhere. Banks are expected to look out for vulnerable customers and scams, and since 7 October 2024, payment firms have had to reimburse most victims of authorised push payment fraud, up to £85,000 per claim. That gives them a strong financial incentive to catch scams before the money leaves.
The Risks and Limits
AI is not a cure-all, and it brings its own problems:
- False positives. Wrongly blocking a legitimate payment or account frustrates customers and can cost sales.
- Bias. Models trained on historical data can reproduce unfair patterns, for example flagging certain groups more often.
- Explainability. When a system makes a decision, businesses need to be able to say why, especially if a customer challenges it.
- Data protection. The UK GDPR’s rules on solely automated decisions with a legal or similarly significant effect have been updated by the Data (Use and Access) Act 2025. According to the Information Commissioner’s Office, which is consulting on updated guidance, organisations must provide safeguards such as information about decisions, the chance to make representations, human intervention and a way to contest the outcome.
- Adversaries adapt. Criminals test and probe systems, so models need constant updating.
For those reasons, most well-run organisations keep humans in the loop for important decisions, such as closing an account or filing a report.
What Businesses Should Ask Before Buying
For smaller businesses considering fraud or verification tools, a few questions help:
- What data does it use, and where is it stored? Check the supplier’s compliance with UK GDPR.
- How are decisions explained? You should be able to see why a transaction was flagged.
- What’s the false-positive rate? Ask for evidence, not marketing claims.
- Can humans override it? A clear escalation route is essential.
- How is it tested and updated? Fraud patterns change quickly.
The Customer’s Side
For consumers, the practical effect is a mix of convenience and friction. Systems that catch fraud can stop a scam before money leaves an account, but they can also trigger extra checks, such as a request to confirm an identity or a payment held for review. Most businesses explain these steps, and providing the documents asked for promptly usually resolves them quickly.
It’s also worth remembering that technology works best alongside good habits: strong passwords, two-factor authentication and caution about unexpected messages remain the best personal protection.
Where It Goes Next
Expect the same technology to spread. Regulators are paying closer attention to how firms use AI, and businesses are under pressure to show that their systems are fair, accurate and accountable. The organisations that do best will be those that treat fraud prevention and customer protection as part of the product, not as an afterthought.
David Prior
David Prior is the editor of Today News, responsible for the overall editorial strategy. He is an NCTJ-qualified journalist with over 20 years’ experience, and is also editor of the award-winning hyperlocal news title Altrincham Today. His LinkedIn profile is here.
