Fraud Risk Mitigation in Real-Time Payments: A Strategic Agent-Based Analysis

Fraud Risk Mitigation in Real-Time Payments: A Strategic Agent-Based Analysis

Katherine Mayo, Nicholas Grabill, Michael P. Wellman

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 157-165. https://doi.org/10.24963/ijcai.2024/18

Whereas standard financial mechanisms for payment may take days to finalize, real-time payments (RTPs) provide immediate processing and final receipt of funds. The speed of settlement benefits customers, but raises vulnerability to fraud. We seek to understand how bank nodes may strategically mitigate fraud risk in RTPs, through investment in fraud detection and restricting payments eligible for real-time processing. To study this, we introduce an agent-based model of the payment network supporting both real-time and standard payments, and define a game among banks and fraudsters. Using empirical game-theoretic analysis, we identify Nash equilibria in nine game configurations defined by network attributes. Our analysis finds that as banks become more liable for fraud, they continue to allow RTPs but are more likely to employ both restrictions and a high level of fraud detection. Fraudsters, in response, switch from targeting only RTPs to attempting fraud with any type of payment and tend to exploit banks where they have historically been most successful. We also conduct a strategic feature gains assessment to further understand the benefit offered by each of the bank's risk mitigation measures, which confirms the importance of selective RTP restrictions. Finally, we find that in equilibrium bank strategic decisions negatively affect fraudsters while minimally impacting customers.
Keywords:
Agent-based and Multi-agent Systems: MAS: Applications
Multidisciplinary Topics and Applications: MTA: Economics
Multidisciplinary Topics and Applications: MTA: Finance