A Multimodal Machine Learning Framework for Credit Card Fraud Detection using Numerical and Temporal Feature Fusion
The rise of digital payments has magnified credit card fraud issues the complexity, scope and therefore the attack strategies have evolved to become an enormous obstacle for the traditional, static (rule based) and single- modal methods of ML in learning dynamic transactional patterns. In this paper we introduced a multimodal fraud detection approach combining the typical numerical features characterizing single transactional entities with their time- series transaction behaviour so that we could achieve higher accuracy in identifying frauds. The former represents properties for each transaction; temporal patterns in user spending are captured by a Gated Recurrent Unit (GRU), a network effectively modelling sequences of events (purchase history in our context) and then combined to finally perform binary classification in which either fraudulent or real transaction class is detected. We use the public domain 284,807 transaction records from credit card dataset; fraud transactions account for 0.172 of the samples. The experiment shows our novel framework (multimodal GRU) outperformed existing ML algorithms and single-mode techniques, having the recall reach 0.89, F1-score value 0.90 and AUC score 0.99. It’s highly scalable and efficient to use in real time monitoring applications. Our proposed method of combining two types of features: a set of single number characteristics and sequence-based information to successfully solve this challenging problem in modern finance.