According to the Office for National Statistics, there were 4.2 million fraud incidents in England and Wales for the year ending March 2025 (ONS). That’s a massive 31% increase year-on-year. It’s the highest number of fraud instances since the ONS records began in 2017.
The issue is that online fraud is becoming so much more sophisticated than it ever was because of artificial intelligence. Fraud is more convincing than ever, so the strategy is emerging to use the same technology for detection and deterrence.
Below, we’ll explore how AI and machine learning (ML) are enhancing fraud detection.
What is machine learning?
Machine learning is a subset of AI. It uses complex algorithms that are essentially constantly learning from the data they’re fed. It’s infinite evolution and understanding.
ML can easily adapt to almost limitless inputs – (see more about this subject on this webpage here – https://www.ovhcloud.com/en-gb/learn/what-is-machine-learning/) – including human instruction as well as datasets. In relation to fraud detection, companies are using ML by training it on patterns of fraudulent activity to detect future attacks.
Why machine learning is leading fraud detection
Fraud is more than dodgy phishing emails or spam calls. It’s texts sent through the same messaging thread as legitimate bank texts, it’s faultlessly mimicking customer behaviour, and it’s using voice recognition and deep fakes. It’s so advanced that it almost seems human.
Traditional rule-based systems that simply flag a transaction aren’t enough. The benefit of ML technology is that it’s constantly learning and adapting – something humans can’t do at the same speed.
How machine learning enhances fraud detection
The issue is that fraudulent attacks are becoming so advanced because of AI tools that humans simply can’t keep up. These ML models can almost instantly review data and transactions to understand what normal user behaviour is. If the pattern even seems slightly suspicious from the accounts’ norm, activity is blocked, or further investigation is requested.
Some of the most innovative ways machine learning enhances fraud detection include:
- Constantly adapting to new fraud tactics
- Real-time detection and alerts
- Reduces false positives
- Empowers human analysts
- Predicts future fraudulent activity
The issue with the advances in fraudulent activity
We’ve already touched on it, but the issues with how advanced fraudulent activity is becoming are why ML is so essential.
Using the same technology, online attackers are creating highly personalised attacks that are too convincing. And the recent emerging deepfake technology is taking this to another level. Online fraud attackers manipulate photos, text messages, videos, etc.
It seems like businesses are often the target and the most vulnerable to deep fake scams. Similar to fraud, deepfakers target businesses because they know there are monetary gains.
And it seems this is only the beginning. With a 31% increase in fraud activity year-on-year from March, it’s evident that the issue is becoming more advanced than ever.
Machine learning is the future of tackling fraudulent activity. Whether they’re using AI tools to generate deepfake attacks or traditional phishing scams, we’re at a point where humans can’t keep up with the volume or the sophistication. It’ll be interesting to see how the attacks advance over the next 12 months.
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