Exploring AI-powered Credit Card Fraud Detection Models: An Application of Capable Guardianship

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Abstract

Protecting banks and their clients from fraudulent activities is a critical concern in the finance industry, leading to the prioritization of fraud detection mechanisms. With the growing prominence of machine learning techniques, there has been a surge in the development of AI-driven systems for identifying fraud. Using a sample of 568,628 anonymized credit card records, the present study examines the cyber guardianship concept from routine activity theory with respect to its utility in reducing the risk for credit card fraud. The results of this study suggest that cyber guardianship protects individuals from credit card fraud victimization. Through increasing detection methods, the proposed capable guardianship strategy can protect online banking users from fraudsters while simultaneously facilitating the fight against fraud.

Authors

• Charles Sinchul Back, The University of Scranton

• Raymond Partin, University of Alabama

• Byeongoh Ahn, Republic of Korea Naval Academy

• Buse Onat, The University of Scranton