Data and Model Centric Approaches for Card Fraud Detection

Card fraud has become a widespread issue as technology advances and credit cards become more popular. Over the last decade, criminals have found diverse ways to steal card information or create duplicates without the cardholder’s knowledge by deploying adaptive approaches against the system. Therefore, detecting suspicious behaviour of a card is critical for preventing fraudulent transactions. However, detecting fraud behaviour is challenging task, given that the transactions’ data are imbalanced, and the adaptive fraudsters’ approaches against the system. In this paper, a combination of data and model centric approaches are proposed for card fraud detection. Starting by data analysis and preprocessing, this was followed by feature engineering stage, then two main family of models were used. The first is an Auto Encoder for anomaly detection, and the second is time series prediction, utilizing Long Short-Term Memory (LSTM) and Convolution Neural Network. The variations of LSTM used include CNN-LSTM, LSTM-Attention mode, and CNN-LSTM with an attention layer. The latter achieved the State-of-the-Art F-score of 93% on the IBM data set. Best F-scores of 94% is also achieved by stacked LSTM on Sparkov dataset.