  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 tree-based 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.
