The case for purpose-built generative AI in fraud prevention

Financial crime has evolved in tandem with the technology designed to combat it, often staying one step ahead. Despite decades of AI advancements in fraud detection, criminals continue to push the boundaries. Over 87.5 million American adults fall victim to scams or financial fraud annually, with roughly one in three adults experiencing such incidents.

The question for financial institutions is not whether AI should be used in fraud prevention, but whether the AI in use is optimized for the current and impending threat landscape.

Gone are the days when data scientists faced computational limitations that prevented them from implementing sound theories. With the advent of GPUs and high-performance computing, new algorithms can now evaluate extensive transaction histories in real-time. This shift allows for sharper, more accurate predictions with fewer false alarms that could delay or halt legitimate transactions, potentially harming customer trust.

A new era of fraud prevention is emerging with the advent of purpose-built generative AI models, tailored specifically for transaction analytics and financial crime detection. These models are not generic but are engineered exclusively for financial transaction data and focused on single, specific tasks. Examples include account takeover detection, scam recognition, mule detection, and first-party misuse detection.

Each model specializes in its area, providing a more comprehensive, accurate, and transparent approach than a single, all-encompassing model. This methodology also extends to other risk decisions, hardship management, collections, and any application where understanding customers can improve engagement, protection, and service.

The future of fraud prevention is here, with enterprises investing in customer fraud protection recognizing the importance of purpose-built models, specialized compute, and AI agents. The math for robust fraud prevention algorithms, developed by AI scientists decades ago, is now ready to be realized with the necessary computational power.

Throughout my career, I have strived to ensure that the industry is prepared to harness the most advanced AI tools as soon as the computing infrastructure becomes available. This ongoing work aims to keep financial institutions at the forefront of AI-driven fraud prevention.