Optimization of Machine Learning Algorithms for Fraud Detection in Electronic Financial Transactions

Abstract

Electronic financial transactions play a pivotal role in the modern financial ecosystem, but they also attract sophisticated fraudulent activities. This research delves into the optimization of machine learning algorithms for fraud detection in electronic financial transactions. The study demonstrates that algorithm optimization significantly enhances detection performance, as evidenced by improved accuracy, reduced false positives, increased recall, precision, and F1-score. The practical implications of these findings are substantial. Enhanced fraud detection algorithms contribute to heightened security for individuals and financial institutions. Reduced false positives streamline transaction verification processes, bolstering customer confidence and operational efficiency within financial institutions. Looking ahead, future research should explore more advanced techniques and algorithms for fraud detection, such as real-time data processing and deep learning. Additionally, policy, legal implications, and data privacy considerations should be integrated into developing more robust fraud detection solutions. Further studies on the impact of regulatory changes on fraud detection represent valuable avenues for future research. By continuously advancing technology and detection approaches, we can better safeguard electronic financial transactions in our ever-evolving digital landscape.