Credit Card Fraud Detection Based on Machine Learning Algorithms

The wide popularity of electronic payment has driven credit cards to become the core payment tool for daily consumption and commercial transactions. However, the continuous expansion of transaction scale has provided more breeding space for fraud, making credit card fraud detection an important issue in the field of financial security. This paper first conducts data exploration through correlation analysis and violin graph analysis, and then constructs multiple machine learning algorithms for performance comparison experiments. The experimental results show that the proposed Transformer-BiGRU algorithm presents significant advantages in all performance indicators and has the most outstanding comprehensive performance. The accuracy rate, recall rate, precision rate and F1 value of this algorithm all reach 98.6%. It is the only model among all the comparison algorithms that has achieved unification and reached the highest level in these four core indicators, fully demonstrating extremely strong classification consistency and reliability. This research achievement provides an efficient and feasible technical solution for credit card fraud detection, which has significant practical value in enhancing the risk prevention and control capabilities of financial transactions and ensuring transaction security.