  There is a growing demand for explainable, transparent, and data-driven
models within the domain of fraud detection. Decisions made by fraud detection
models need to be explainable in the event of a customer dispute. Additionally,
the decision-making process in the model must be transparent to win the trust
of regulators and business stakeholders. At the same time, fraud detection
solutions can benefit from data due to the noisy, dynamic nature of fraud and
the availability of large historical data sets. Finally, fraud detection is
notorious for its class imbalance: there are typically several orders of
magnitude more legitimate transactions than fraudulent ones. In this paper, we
present Deep Symbolic Classification (DSC), an extension of the Deep Symbolic
Regression framework to classification problems. DSC casts classification as a
search problem in the space of all analytic functions composed of a vocabulary
of variables, constants, and operations and optimizes for an arbitrary
evaluation metric directly. The search is guided by a deep neural network
trained with reinforcement learning. Because the functions are mathematical
expressions that are in closed-form and concise, the model is inherently
explainable both at the level of a single classification decision and the
model's decision process. Furthermore, the class imbalance problem is
successfully addressed by optimizing for metrics that are robust to class
imbalance such as the F1 score. This eliminates the need for oversampling and
undersampling techniques that plague traditional approaches. Finally, the model
allows to explicitly balance between the prediction accuracy and the
explainability. An evaluation on the PaySim data set demonstrates competitive
predictive performance with state-of-the-art models, while surpassing them in
terms of explainability. This establishes DSC as a promising model for fraud
detection systems.
