By RiskTech Radar
Published on Nov 3, 2024
In today’s digital economy, fraud has become more sophisticated and pervasive than ever before. As fraudsters evolve and make use of advanced technologies and techniques, traditional methods of detecting and preventing fraud often struggle to keep pace. Enter Artificial Intelligence (AI) – a transformative technology poised to reshape how organizations combat fraud. But what exactly is AI bringing to the table, how is it impacting the fraud detection market, and is it actually new?
It’s worth noting that while AI is sparking significant change in the fraud detection market, machine learning (ML) and other AI-based techniques aren’t entirely new. Consumer banking, for instance, has leveraged ML in fraud detection for years. Banks have long used ML models to identify suspicious payments, immediately notifying the customer and then learning from all customer responses - it is a core component of their anti-fraud strategies.
"Unsupervised learning can go further and even detect previously unknown fraud patterns." - FT
However, the AI techniques available today are more sophisticated, encompassing advanced techniques like deep learning, natural language processing (NLP), and generative AI (GenAI). These new capabilities are pushing fraud detection forward, allowing organizations to analyze more and different types of data, respond in real-time, and uncover insights traditional models might miss.
A significant advantage of AI, particularly ML, is its ability to uncover patterns and connections that may not be immediately obvious to humans or rule-based systems. For example, a machine learning model might detect a correlation between a specific sequence of actions on a website and a type of emerging fraud. These novel patterns are often unknown to human analysts, and without AI, organizations wouldn’t know to set rules for them. This capability allows AI-driven systems to flag suspicious behaviors that rules alone might miss, keeping organizations one step ahead of fraudsters who constantly adapt their techniques.
Machine learning models improve over time as they encounter more cases, making them more accurate at detecting fraud than static rule-based systems. This adaptability enables ML models to identify complex trends that would be challenging for humans to recognize. For example, they can analyze combinations of transaction amounts, locations, and time stamps, spotting subtleties that indicate fraud. The result? Fewer false positives and more precise alerts, helping analysts focus on genuine cases and reducing unnecessary investigations.
"At HSBC, we check about 1.35 billion transactions for signs of financial crime each month, across 40 million customer accounts – we’re using artificial intelligence (AI) to help us do this." - HSBC
Traditional fraud detection relies primarily on structured data, like transaction logs. However, AI is changing this by enabling organizations to analyze unstructured data sources at scale. NLP can process text from call transcripts, while advanced models can even analyze voice data for stress signals or anomalies. For instance, AI can process thousands of hours of customer support calls, flagging phrases and vocal patterns indicative of fraudulent intent. Some organizations are even experimenting with video analysis to detect body language cues associated with deception, adding a whole new dimension to fraud detection.
Generative AI (GenAI) models, like large language models, are proving to be valuable tools for accelerating fraud investigations. By quickly sifting through vast amounts of data, GenAI can summarize cases, draft investigation reports, and even help analysts identify next steps. This capability allows fraud teams to close cases faster, maintain detailed audit trails, and respond to regulatory requirements more efficiently. GenAI is also emerging as a tool for scenario simulation, enabling fraud teams to test their models against hypothetical fraud scenarios and refine their defenses accordingly.
"GenAI is set to play a pivotal role in how fraud experts manage and analyse vast amounts of data." - CTO of Forter
Nearly all vendors operating in the fraud detection space have been ensuring AI is being leveraged in their products. New vendors and products are going 'AI first' whereas existing products have either been quickly adding AI elements to their tech, or they've been increasing their marketing to highlight the AI that was already in there.
AI systems require extensive data, often involving sensitive customer information. Ensuring data privacy and meeting regulatory compliance is paramount, especially in sectors like finance. Organizations need to implement robust governance policies to safeguard customer information and avoid regulatory penalties.
AI models can unintentionally inherit biases from the data they’re trained on. For example, if historical data contains biases, these can be perpetuated in the model’s outputs. Regular audits and careful data selection can mitigate these risks, but fairness remains a critical concern in AI-driven fraud detection.
Some advanced AI models, especially deep learning algorithms, can be challenging to interpret. For compliance and transparency, organizations must balance accuracy with interpretability, particularly in regulated industries. Interpretable AI models, combined with human oversight, can help strike a balance between high performance and accountability.
As AI becomes more accessible, even smaller organizations are adopting AI-powered fraud detection tools. This democratization of AI enables more companies to benefit from advanced detection capabilities, increasing pressure on fraudsters to adapt.
Blockchain technology’s transparency and immutability are gaining traction in fraud prevention. When combined with AI, blockchain could add a layer of security to fraud detection systems, further complicating fraudsters’ efforts to manipulate data or transactions.
As fraudsters start using AI to evade detection, adversarial machine learning is emerging as a way to counteract these efforts. In adversarial machine learning, models are trained to identify and counteract attempts by fraudsters to manipulate them. As fraud detection becomes increasingly AI-driven, adversarial machine learning will play a critical role in staying ahead of bad actors.
AI is undeniably transforming the fraud detection market, providing tools to detect fraud in real-time, identify novel fraud patterns, analyze unstructured data, and accelerate investigations. While some AI techniques, like machine learning, have been around for years, new advances in AI continue to raise the bar, equipping organizations to stay one step ahead of fraudsters. However, challenges like data privacy, fairness, and interpretability remain critical considerations for organizations adopting AI for fraud detection.
As AI continues to evolve, its role in fraud detection will become even more significant. For organizations looking to remain resilient against fraud, embracing AI isn’t just a choice – it’s a strategic necessity.