Identifying ATM Fraud Transactions in Thailand using Location-based Grouping and Behavior Feature
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Abstract
Financial fraud causes a major loss to a bank. The challenge of classifying fraud is a high true positive rate while keeping the number of false positives as low as possible. One difficulty is the unbalanced size of the labeled data that causes a low detection rate and a high false-positive rate. We present a method to sample the data to cope with the unbalance problem in the fraud detection problem. The location feature is applied to separate accounts into ‘local-only’ and ‘has-abroad’. The proposed feature extraction can separate many fraud transactions from legitimate transactions. To differentiate the fraud from the legitimate transactions, the fraud can be considered as an outlier. Transformation functions, the deviation, the risk, and the probability features are applied in this work to both numeric and non-numeric features. The experimental result shows that the location-based separation together with the proposed features achieves higher TRP and lower FPR than not dividing the group. It achieves a true positive rate of 75.00% for ‘local-only’ and 100% for ‘has-abroad’. The lowest false positive rate is 8.23% for ‘has-abroad’. Comparing the efficiency of the proposed features, the true positive rate is improved from 56.25% to 75.00%.
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