Behavioral Sequence Modeling for Real-Time Financial Fraud Prevention

Abstract

The spread of mobile money has also been coupled with increased advanced frauds that have penetrated the security systems. The current fraud detection systems which usually involve rule-driven logic or static transactional capabilities cannot detect fraudulent activities which resemble legitimate user actions. The paper will present a new deep learning model to detect mobile money frauds using the temporal analysis of sequence of transactions. The hybrid recurrent autoencer architecture methodology makes use of a synergistic combination of a Recurrent Neural Network (RNN) and a Long Short-Term Memory (LSTM) network. The structure is double-layered to acquire immediate and short-term contextual patterns as well as sophisticated, far-off connections in the financial history of a user. The model is self-supervised and trained only on legitimate transaction data forcing it to infer a deep and implicit code of normal behavioral grammar. Fraud is then detected as something suspicious. In an event where a new transaction takes place, the model determines the probability of the next transaction set depending on the earlier sequence. The predicted and actual transaction can be compared, the deviation between them in terms of the Mean Squared Error in the model is a score of the anomaly. All the transactions that go above a calibrated threshold are put on the alert as possible fraudulent. This method is able to provide a more robust defense mechanism of mobile financial platforms because it concentrates on the sequential integrity of user behavior, not on individual events, and, as a result, is willing to identify more complex cases of fraud, such as account takeovers and social engineering attacks.