[KDD 2020] Interleaved Sequence RNNs for Fraud Detection
Aug 13, 202044 views
Payment card fraud causes multibillion dollar losses for banks and,merchants worldwide, often fueling complex criminal activities.,To address this, many real-time fraud detection systems use treebased models, demanding complex feature engineering systems to,efficiently enrich transactions with historical data while complying,with millisecond-level latencies. In this work, we do not require,those expensive features by using recurrent neural networks and,treating payments as an interleaved sequence, where the history of,each card is an unbounded, irregular sub-sequence. We present a,complete RNN framework to detect fraud in real-time, proposing,an efficient ML pipeline from preprocessing to deployment. We,show that these feature-free, multi-sequence RNNs outperform,state-of-the-art models saving millions of dollars in fraud detection,and using fewer computational resources.