Automating the Detection of Fraudulent Activities in Online Banking Service.

Banks offering Online Banking services need to detect and prevent unauthorized electronic funds transfers to reduce financial crime risk. They monitor online banking transaction histories and use their own methods to detect and prevent unauthorized electronic fund transfers. However, unauthorized electronic fund transfers by criminals have not been eliminated. The average rate of false positives in the transaction monitoring systems installed in Japanese banks is up to 99%, indicating that the monitoring systems are not fully functional. Furthermore, the personnel responsible for fraud detection must manually check a large number of false positives, making it difficult for operators to be productive in their assigned tasks. Based on the above background, we develop a method to detect unauthorized electronic fund transfers and suspicious transactions with high accuracy using machine learning algorithms and evaluate its accuracy. Specifically, a supervised machine learning algorithm is applied to detect fraudulent transactions automatically. We evaluated the proposed method on a large set of online banking transaction data provided by a major Japanese bank for the period March 2019 to May 2020. We demonstrated that our approach could detect fraudulent activity with extremely high accuracy; FPR=0.000 and FNR=0.005 can be achieved for a security policy that minimizes false positives.