Machine Learning Perspective: Fraud Payment Transaction Detection

Abstract

Online banking transaction fraud occurs when fraudulent activity is initiated by a criminal. Such as seizing accounts and hacking points to execute online fund transfer mechanisms. This scenario is a main challenge for the upcoming data processing that is traveling only online. In today’s scenario, most of the work has become digital. In such cases, machine learning algorithms must be ex-traordinary to take stringent security measures to transfer such funds over a public channel. The paper analyzes some machine learning techniques such as naive Bayes and support vector machines to prevent loss. The machine has been used based on real data and can reduce losses manifold. After processing, the optimization reached 95 percent in terms of accuracy. The implementation can improve business operations to move the funds online while reducing overall risk.