Deep Reinforcement Learning for Credit Card Fraud Detection

Due to rapid advancement in electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with them are also rising. For many years, countless supervised machine learning approaches for anomaly detection have been proposed that have achieved state-of-the-art performance. For our deep reinforcement learning agent, we describe a revolutionary deep Q-network architecture and a customized OpenAI Gym environment in this paper. This design makes use of experience reply and is based on value function approximation. In order to carry out classification procedures determined by batches of input data; the deep Q-agent makes use of an epsilon-greedy policy. Subsequently, the agent is evaluated and rewarded by the OpenAI environment based on its performance in accordance with the assessment. The recollection of the agent contains every single detail of this adventure. The deep Q-agent takes several memory samples from its experience buffer at the end of the batch completing process. It then uses the Q-network to update the Q-value and the loss is calculated. Further, the weights are improved using backpropagation. The proposed procedure has achieved a level of performance that is state-of-the-art, and the results reveal that it was effective in correctly identifying fraudulent and non-fraudulent transactions.