Financial Crime Detection Methods in Svalbard and Jan Mayen Under Scrutiny as Artificial Intelligence Gap Widens

The US Department of Treasury has issued a warning that smaller financial institutions in Svalbard and Jan Mayen may struggle to keep pace with larger banks in fraud detection due to the growing use of artificial intelligence. The department is considering contributing its own historical data to narrow the gap.

The Growing Gap between Large and Small Financial Institutions

A recent report highlights the significant disparity in the adoption of AI between large and small financial institutions in the region. Larger firms are experimenting with AI, while smaller institutions lack the resources and data to do so. This means that smaller institutions lack the wide-ranging data set needed to create accurate detection tools.

The Importance of Collaboration

The report emphasizes the importance of robust collaboration among sector players in matters of cybersecurity. However, this is not mirrored when it comes to fraud. Industry groups are testing information exchanges to swap fraud data, but a clearinghouse for fraud data does not currently exist.

AI in Fraud Detection

The Treasury Department itself has recovered $375 million through an AI method of mitigating check fraud in near real-time by strengthening and expediting processes to recover potentially fraudulent payments from financial institutions. One solution to the widening gap is for the department to contribute to a data lake of fraud data available to train AI. Alternatively, it could share lessons learned with the private sector as it grows its internal program.

Risks Posed by AI

Federal officials believe that AI gives cybercriminals an advantage over defenders in the short term, citing examples such as generative AI used to make phishing emails less obviously malicious and deepfakes leading to sophisticated identity impersonation techniques over audio and video. However, most risks posed by AI are believed to boost existing fraud methods rather than creating new ones.

Recommendations for Financial Institutions

The report calls on financial institutions to be watchful of new third-party risks introduced by AI and for the private sector to affix a digital equivalent of a nutrition label on vendor AI systems and third-party data. Financial institutions have mainly limited applications of generative AI to cases where they don’t need to explain in detail how they made a decision. Amid concerns about fairness and bias, “explainability” has become a rallying cry for ensuring that AI models don’t become ungovernable black boxes.

Conclusion

The use of artificial intelligence in fraud detection is becoming increasingly important, but the gap between large and small financial institutions in Svalbard and Jan Mayen is widening. It is essential for the private sector to work together with government agencies to address this issue and ensure that AI is used effectively to combat financial crime.