According to a recent report from the U. S. Treasury on managing artificial intelligence (AI)-related risks in the financial sector, larger financial institutions have a competitive advantage over smaller ones in AI-based fraud prevention. The report highlights the lack of data available to train AI models for fraud prevention, particularly due to insufficient data sharing among firms. Smaller financial institutions typically lack the necessary internal data and expertise to develop their own anti-fraud AI models, while bigger institutions have access to extensive historical data. The report emphasizes the need for collaboration and data sharing among financial institutions to enhance the training of anti-fraud AI and machine learning models.

It also acknowledges that fraudsters themselves have been leveraging AI and ML technologies, making collaboration among FIs even more crucial. PYMNTS Intelligence research shows that FIs rely on a combination of in-house fraud prevention systems, third-party resources, and new technologies to combat fraud. While the use of AI and ML in fraud prevention is increasing, the report notes that developing these tools can be costly, resulting in only a small percentage of FIs building their own AI and ML technologies for fraud prevention. Collaboration with third-party vendors is common in acquiring fraud-fighting tools. The report concludes that combining technology with critical thinking is essential in effectively dealing with financial crime, adopting a "Robocop, not Terminator" approach.