As we mark Fraud Awareness Week, it’s the perfect time to look at how the landscape of financial crime is shifting – and how banks and insurers can move beyond reactive measures to adopt proactive, data-driven strategies.
In the UK in 2024, the banking industry lost £1.17 billion to fraud across more than 3.3 million incidents, according to UK Finance. Meanwhile, global research from Juniper Research predicts financial institutions could face losses of $58.3 billion annually by 2030.
For insurers and banks, these figures represent more than financial loss – they reflect significant reputational, operational and regulatory risks.
In this blog, we explore how machine learning can help combat these risks, saving institutions time, money and customers.
What is machine learning?
Machine learning is a branch of AI that allows systems to learn from data rather than relying on fixed, rule-based programming. Instead of being explicitly told what to look for, machine learning analyses historical and real-time data to identify patterns, make predictions and flag anomalies. This makes it particularly valuable for fraud detection, where new threats constantly emerge and evolve.
How you can use machine learning to detect fraud
Machine learning can be applied to fraud detection in several ways.
Supervised machine learning is the most common starting point. Here, algorithms are trained on historical data labelled as “fraudulent” or “legitimate”. For example, in credit card fraud detection, a supervised model can learn patterns like transactions happening seconds apart in different countries or a sudden high-value purchase following a series of small, local ones. Once deployed, the model scores new transactions in real time, flagging high-risk activity for review or automated blocking. This same approach can be used to prevent account takeovers (where a user’s normal behaviour suddenly changes like with new devices, different log-in times or unusual transfer patterns).
However, fraudsters rarely stand still. When entirely new attack methods appear, such as synthetic identities or device-spoofing, there may be little to no labelled data available. This is where unsupervised learning becomes invaluable. Instead of relying on pre-defined examples, it scans data to find anomalies or outliers that deviate from normal behaviour. In insurance claims processing, for example, an unsupervised model might flag clusters of claims submitted from the same IP address, or detect an employee initiating payments that don’t match their typical patterns. It’s an approach well-suited to identifying emerging threats that traditional systems can’t yet describe.
For even more complex scenarios, deep learning takes machine learning a step further. Using layered neural networks, deep learning models can process vast, intricate datasets and uncover relationships that are too subtle or non-linear for humans or simpler algorithms to detect. In account takeover prevention, for instance, a deep-learning model could analyse biometric or behavioural signals like typing rhythm, mouse movements or time-of-day activity to tell the difference between a legitimate user and a fraudster. Similarly, banks and insurers can use deep learning to spot insider fraud, where a trusted employee’s activity patterns shift gradually over time.
The benefits of machine learning over traditional fraud detection
Traditional fraud detection methods often rely on static, rule-based systems, for example, “flag all transactions over £5,000” or “block log-ins from specific countries”. While simple to implement, these systems are rigid. They can’t easily adapt to new fraud tactics or identify the nuanced patterns that modern criminals exploit.
Machine learning, by contrast, offers a dynamic and adaptive approach. It can:
- Continuously learn and adapt as new data comes in, automatically updating its understanding of what “normal” looks like
- Scale and respond in real-time, analysing millions of transactions or log-ins per second to detect suspicious behaviour instantly
- Reduce false positives, allowing more genuine transactions to proceed without unnecessary friction for customers
- Spot subtle, multi-factor patterns that humans or static rules would overlook
- Take a proactive stance, identifying emerging or unfamiliar threats before they become widespread
During Fraud Awareness Week and beyond, one message is clear: fraud is no longer a static threat. It adapts, evolves and scales as quickly as technology itself. Relying solely on manual reviews or rigid rule-sets is no longer sustainable. Machine learning gives the financial sector a chance to stay one step ahead, turning fraud detection from a defensive exercise into a proactive advantage.
If you’re looking to strengthen your defences, explore how Engage Hub’s advanced analytics can enhance your current fraud detection and compliance initiatives. Learn more in this whitepaper.
