The power of machine learning and AI in fraud detection

The power of machine learning and AI in fraud detection

The biggest challenge faced by organisations today is not the increasing volume of financial crime, but the creativity and sophistication of fraud attacks. Whether we're talking about credit card fraud, identity fraud, payment fraud, application fraud, or other types of financial fraud, criminals are constantly developing new methods to exploit systems and gain access to funds.

In their bid to battle these threats, organisations are seeing more missed frauds and higher false positive rates leading to genuine customers being stopped.

The only way to prevent fraud is to stay one step ahead – which is precisely what AI and, more specifically, machine learning can help organisations do.

This article will explain how AI and machine learning work in fraud detection, an example of how machine learning is combating identity fraud, and its advantages in helping businesses stay ahead of fraud.

Machine learning in fraud detection

More businesses are using some form of AI, especially with the explosive rise of generative tools like ChatGPT. However, what many don’t realise is that AI is most commonly used in the form of machine learning.

Machine learning is a subset of AI that enables a system to learn using large amounts of data. A machine learning system can autonomously identify patterns within the data and develop algorithms to make decisions based on the learnings.

So, how does machine learning for fraud detection work?

Machine learning uses advanced algorithms that monitor datasets to identify patterns, detect anomalies, and stop fraudsters before they gain access.

What makes machine learning so powerful in fraud detection is its ability to evolve and learn. Instead of operating based on set protocols, it can learn from its analysis of historical transaction data and fraud patterns and adjust its rules to stop threats it may never have encountered before.

The more it learns and becomes familiar with the organisation's systems, transaction patterns, and the fraudsters' techniques, the more effective the machine learning model becomes in fraud detection and prevention. This improved accuracy means it continuously reduces the number of false positives, meaning fewer genuine users are blocked.

How machine learning models can be used in identity fraud detection

Detecting identity fraud at the onboarding stage is critical to preventing financial crimes, and that's where machine learning can be highly effective.

Machine learning models can be trained on historical data (or training data) to recognise and flag the signs of fraudulent activities. In this case of identity fraud, this requires a large amount of identity verification data – specifically, data that shows the early signs of identity theft and takeover.

That's what GBG has used to create GBG Alert, an early fraud detection model designed to detect fraud at onboarding. The machine learning model draws on millions of identity verifications processed by GBG across industries, including banking and telecommunications, that show early indicators of identity theft and takeover. By leveraging this unique cross-industry data, it has built rules for the market across Australia to accurately predict and identify fraud early. Every suspicious identity verification is tagged with an explainable rule code, so based on the results, organisations can then apply a step-up identity process or reject an application altogether.

GBG Alert is has achieved up to 89% accuracy in detecting identity takeover, money muling and other fraud events. And because machine learning is a dynamic tool, constantly learning and adapting, GBG Alert improves over time as it encounters new data. This makes it more effective than static rule-based fraud detection systems.

The advantages of AI and Machine Learning in fraud detection

Real-time detection

Speed is critical in fraud detection. Where human analysts have limits on how quickly they can complete investigations, machine learning systems can process incoming data and block new threats in real-time. Because it works rapidly, machine learning for fraud detection doesn't impact the user experience and can enable a frictionless customer experience in the digital onboarding and transaction process.

Advanced pattern recognition

One of the biggest strengths of AI in fraud detection is its ability to identify patterns and relationships within data that human analysts may not pick up. This can help prevent more sophisticated identity theft, credit card fraud or other financial crimes.

Keep up with evolving threats

AI is an essential tool for keeping up with evolving threats. And in a world where threats are changing rapidly, this is critical for organisations that want to stop fraud. For example, GBG's machine learning systems can detect 80 per cent more frauds that may have otherwise bypassed traditional rule-based approaches.

Learn and improve over time

The more AI works and the more data it processes, the better it gets at detecting and preventing fraud. That makes machine learning such a powerful and adaptive solution to combat financial fraud's complex and rapidly evolving nature. As fraudsters change tactics, machine learning models don't become null and void. Instead, they can adapt rapidly based on new data and continue to detect emerging fraud patterns.

Reduce false positives

False positives are common with traditional fraud detection methods. They can prevent genuine customers from accessing products and services and be destructive to customer relationships, not to mention the extra time staff must spend on investigations. However, AI is more accurate when distinguishing between genuine and fake transactions. GBG's machine learning models can reduce false positives by at least 40 per cent.

Free up resources

With AI, employees can spend less time being reactive and investigating threats. Instead, they can spend their time performing deeper investigations and add value to other fraud detection projects.

Raise your game

Artificial intelligence and machine learning not only enhance the efficiency of fraud detection but also provide a proactive approach to safeguarding individuals and organisations from the pervasive threat of identity fraud. When integrated with other analytical methods, including human expertise, its unmatched fraud detection capabilities make it a vital element of your fraud prevention strategy.

Learn more about how GBG Alert can help you raise your game in fraud detection.

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