AI-Powered Fraud Detection: Secure Online Transaction Monitoring Using Machine Learning
Authors:-Mrs.G.Tejasri Devi, T.Sai Srinath, G.Naga Kastusi, V.Anshitha, G.Janitha Sree, G.Jashwitha
Abstract-Fraud detection remains one of the most critical challenges in financial transactions, driving on going research and the adoption of advanced technologies such as machine learning. Financial transaction fraud detection aims to explore and compare various machine learning approaches to assess their effectiveness, challenges, and potential future developments comprehensively.This paper begins by highlighting the importance of fraud detection in financial transactions, emphasizing the widespread impact of fraudulent activities on individuals, businesses, and the overall economy. While traditional fraud detection methods have been valuable, they often struggle to counter increasingly sophisticated and evolving fraudulent schemes. As a result, more advanced techniques are required to enhance detection accuracy.Machine learning-based approaches have emerged as a promising solution, enabling algorithms to analyse vast amounts of transactional data and identify patterns indicative of potential fraud. In particular, supervised learning techniques—such as logistic regression, decision trees, and support vector machines—have gained significant popularity in fraud detection due to their ability to classify transactions as legitimate or fraudulent based on historical data.
