Genetic Algorithm Hyperparameters Optimization for Auto-Encoder to Enhance Credit Card Fraud Detection

In recent years, the increasing use of credit cards has led to a dramatic rise in fraudulent transactions. Identifying credit card fraud is crucial for preventing financial institutions and their customers from incurring substantial financial losses and for maintaining customer trust. Fraud detection systems play a key role in preventing unauthorized transactions. Auto-encoder networks, known for their ability to learn compact representations of data, can leverage this capability to identify anomalous transactions, which are typically indicative of fraud. However, their performance is often sensitive to hyperparameter choices, especially in imbalanced datasets. To address this, a genetic algorithm is utilized to dynamically optimize the hyperparameters of these networks, significantly improving fraud detection. This paper proposes using an optimized auto-encoder network, augmented with the genetic algorithm, to detect credit card fraud. On the Credit Card Fraud (CCF) dataset, the model achieved an F-score of 91.04 % and a Matthews correlation coefficient (MCC) of 0.8282, surpassing some baseline methods such as OCSVM, OCNN and COPOD. These results demonstrate the effectiveness of the genetic algorithm in improving the performance and robustness of fraud detection systems.