Improving Financial Fraud Detection System with Advanced Machine Learning for Predictive Analysis and Prevention

Credit card fraud is a major problem for financial institutions. Credit card fraud costs over one million people every year across several different nations. Research studies involving the study of actual credit card data are few and few between because of privacy issues. In order to prevent financial losses and make sure that transactions are safe, CCFD is an essential application in financial systems. This paper focuses on the analysis of financial fraud detection system that uses ML algorithms with special emphasis on the CCFD dataset. The system methods for increasing the model’s accuracy consist of data preprocessing, feature engineering, and class balancing with Borderline-SMOTE. LSTM model improves the performance of the competitors’ algorithms with 99.9% accuracy, precision80.3%, a recall92.1% and F1 - score85.8%. Regarding the trade-off among accuracy and recall rate, LSTM is superior to other models from the fraud detection portfolio, including DT, RF, and SVM. Continuous real-time data, CNNs and other forms of DL may be further explored in future research to extend the detection of fraud. More improvements could also center on model interpretation and bringing up the models’ suitability for practical utility in big conventional financial structures.