Exploring Machine Learning Techniques for Fraud Detection in Financial Transactions

Abstract

Fraud detection is a pivotal task in the financial sector to mitigate fraudulent activities and safeguard the integrity of financial systems. As traditional and mainstream methods of fraud detection become less efficient, there is an escalating need for innovative techniques. As showcased in the study, Machine learning has proved to be a promising technique for automated fraud detection, maximizing its capability to evaluate large datasets and pinpoint complex patterns. This report explored the landscape of machine learning in fraud detection, targeting to provide a comprehensive comprehension of its potential and limitations. The research highlighted the importance of examining machine learning techniques for fraud identification in financial transactions, bearing in mind the evolving nature and complexities of fraudulent activities. Considering the overall performance and evaluation metrics, the Random Forest model proved as the most efficient algorithm for the provided fraud detection challenge. That it achieved the highest precision, accuracy, and recall rates, demonstrating its superior capability to pinpoint both non-fraudulent and fraudulent transactions accurately.