FRAUD DETECTION USING MACHINE LEARNING ALGORITHM

ABSTRACT Financial fraud is a growing global challenge that results in significant economic losses for individuals and institutions. Traditional rule-based detection systems often struggle to keep pace with the rapidly evolving and sophisticated nature of fraudulent schemes. This research addresses this issue by exploring the efficacy of Machine Learning (ML) algorithms for automated and proactive fraud detection within transactional data. The study implements and evaluates classification models on a representative dataset, focusing on techniques such as data preprocessing, feature engineering, and rigorous model training. Performance is assessed using key metrics like accuracy, precision, recall, and the F1-score to ensure the model is robust and minimizes false positives while maximizing the detection rate of actual fraud cases. The findings demonstrate that the selected ML approach provides superior predictive power and detection efficiency compared to conventional methods, offering a robust and scalable solution for mitigating financial risks. This work recommends the practical integration of Machine Learning into existing security protocols to significantly enhance the efficiency and effectiveness of modern fraud monitoring systems.