Credit Card Fraud Detection Using Improved Deep Learning Models

Abstract

Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud. The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision, leading these models to be effective in accurately predicting credit card fraud. The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy (99.2%), detection rate (93.3%), and area under the curve (96.3%). These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.