The Way of Machine Learning Based Solicit for Detecting Deceit in Online Based Transaction System with Security

Detection of fraud still remains one of the most important challenges that continue to stir up research and following implementation of advanced technologies in the field of financial transactions, like machine learning. In this respect, the financial transaction fraud detection seeks to find and compare different approaches in machine learning and understand effectiveness, challenges, and potential directions of future development in a comprehensive way. The paper outlines in the beginning the significance of fraud detection in transactions of finance. It reflects on the general impacts of fraud such as the negative effects on individuals, businesses, and the economy at large due to fraudulent activities. Traditional methods to detect fraud are very valuable; however, they usually fall short against such schemes that are either sophisticated or keep evolving, for which even more sophisticated methods are required. However, with machine learning-based solutions in fraud detection, it is now offering some promise, as these solutions can leverage algorithms processing huge volumes of transactional data to pinpoint any pattern signaling probable fraudulent behavior. Supervised learning techniques, such as logistic regression, decision trees, and support vector machines, have gained great popularity in fraud detection.