Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection

The fraud detection literature unanimously shows that the use of a cardholder's transaction history as context improves the classification of the current transaction. Context representation is usually performed through either of two approaches. The first, manual feature engineering, is expensive, restricted, and hard to maintain as it relies on human expertise. The second, automatic context representation, removes the human dependency by learning new features directly on the fraud data with an end-to-end neural network. The LSTM and the more recent Neural Feature Aggregate Generator (NAG) are examples of such an approach. The architecture of the NAG is inspired by manual feature aggregates and addresses several of their limitations, primarily because it is automatic. However, it still has several drawbacks that we aim to address in this paper. In particular, we propose to extend the NAG in the following two main manners: (1) By expanding its expressiveness to model a larger panel of functions and constraints. This includes the possibility to model time constraints and additional aggregation functions. (2) By better aligning its architecture with the domain expert intuition on feature aggregates. We evaluate the different extensions of the NAG through a series of experiments on a real-world credit-card dataset consisting of over 60 million transactions. The extensions show comparable performance to the NAG on the fraud-detection task, while providing additional benefits in terms of model size and interpretability.