The Unique AI Cybersecurity Challenges in the Financial Sector

Fighting Fraud

The Treasury Department also outlined several other AI-related challenges in the financial sector, including a lack of consistency across the industry for defining AI as well as questions about the future of regulation of AI in the financial services sector, and the potential for regulatory fragmentation.

The report also highlighted how AI is upending fraud prevention for financial institutions. While AI technologies are redefining fraud, it is also empowering defense teams with the tools to improve their own anti-fraud measures. The report cited one large firm that said it developed AI models trained on its own internal data that allowed the company to reduce fraud activity by 50 percent, for instance.

However, only the larger financial institutions, with lots of resources and internal, historical data on hand, are able to develop these models for fraud prevention. Smaller institutions, on the other hand, don’t have the level of internal data, expertise or resources necessary to build their own models, creating a gap in fraud prevention overall. The Treasury Department worried that fraud activity that’s been blocked by models for larger institutions would instead shift to smaller institutions without these same abilities.

“Except for certain efforts in banking, there is limited sharing of fraud information among financial firms,” said the report. “A clearinghouse for fraud data that allows rapid sharing of data and can support financial institutions of all sizes is currently not available. The absence of fraud-related data sharing likely affects smaller institutions more significantly than larger institutions.”