The large variety of digital payment choices available to consumers today has
been a key driver of e-commerce transactions in the past decade. Unfortunately,
this has also given rise to cybercriminals and fraudsters who are constantly
looking for vulnerabilities in these systems by deploying increasingly
sophisticated fraud attacks. A typical fraud detection system employs standard
supervised learning methods where the focus is on maximizing the fraud recall
rate. However, we argue that such a formulation can lead to sub-optimal
solutions. The design requirements for these fraud models requires that they
are robust to the high-class imbalance in the data, adaptive to changes in
fraud patterns, maintain a balance between the fraud rate and the decline rate
to maximize revenue, and be amenable to asynchronous feedback since usually
there is a significant lag between the transaction and the fraud realization.
To achieve this, we formulate fraud detection as a sequential decision-making
problem by including the utility maximization within the model in the form of
the reward function. The historical decline rate and fraud rate define the
state of the system with a binary action space composed of approving or
declining the transaction. In this study, we primarily focus on utility
maximization and explore different reward functions to this end. The
performance of the proposed Reinforcement Learning system has been evaluated
for two publicly available fraud datasets using Deep Q-learning and compared
with different classifiers. We aim to address the rest of the issues in future
work.

该研究借助强化学习的思想，将欺诈检测问题转化成一个序列决策问题，并探索了不同奖励函数的效果，其性能基于深度 Q 学习算法在两个公开欺诈数据集上进行了评估与比较。