  Fraud detection systems (FDS) mainly perform two tasks: (i) real-time
detection while the payment is being processed and (ii) posterior detection to
block the card retrospectively and avoid further frauds. Since human
verification is often necessary and the payment processing time is limited, the
second task manages the largest volume of transactions. In the literature,
fraud detection challenges and algorithms performance are widely studied but
the very formulation of the problem is never disrupted: it aims at predicting
if a transaction is fraudulent based on its characteristics and the past
transactions of the cardholder. Yet, in posterior detection, verification often
takes days, so new payments on the card become available before a decision is
taken. This is our motivation to propose a new paradigm: posterior fraud
detection with "future" information. We start by providing evidence of the
on-time availability of subsequent transactions, usable as extra context to
improve detection. We then design a Bidirectional LSTM to make use of these
transactions. On a real-world dataset with over 30 million transactions, it
achieves higher performance than a regular LSTM, which is the state-of-the-art
classifier for fraud detection that only uses the past context. We also
introduce new metrics to show that the proposal catches more frauds, more
compromised cards, and based on their earliest frauds. We believe that future
works on this new paradigm will have a significant impact on the detection of
compromised cards.
