  Machine learning models have widely been used in fraud detection systems.
Most of the research and development efforts have been concentrated on
improving the performance of the fraud scoring models. Yet, the downstream
fraud alert systems still have limited to no model adoption and rely on manual
steps. Alert systems are pervasively used across all payment channels in retail
banking and play an important role in the overall fraud detection process.
Current fraud detection systems end up with large numbers of dropped alerts due
to their inability to account for the alert processing capacity. Ideally, alert
threshold selection enables the system to maximize the fraud detection while
balancing the upstream fraud scores and the available bandwidth of the alert
processing teams. However, in practice, fixed thresholds that are used for
their simplicity do not have this ability. In this paper, we propose an
enhanced threshold selection policy for fraud alert systems. The proposed
approach formulates the threshold selection as a sequential decision making
problem and uses Deep Q-Network based reinforcement learning. Experimental
results show that this adaptive approach outperforms the current static
solutions by reducing the fraud losses as well as improving the operational
efficiency of the alert system.
