Detection of fraud in IoT based credit card collected dataset using machine learning

Abstract

Due in large part to the proliferation of electronic financial transactions, credit card fraud is a serious problem for customers, merchants, and banks. For this reason, a novel approach is offered to fraud detection that makes use of cutting-edge ML methods in an IoT setting. The method in this paper employs a carefully selected set of cutting-edge ML algorithms specifically designed to handle the complexities of fraud detection, in contrast to older approaches that have difficulty adapting to shifting fraud patterns. In order to address the many facets of the problem, the methodology employs a large collection of ML models. These models include deep neural networks, decision trees, support vector machines, random forests, and clustering methods. This paper provides a solution that is able to detect fraudulent activity in real time by efficiently analyzing massive amounts of transactional data thanks to the power of big data processing and cloud computing. The model is able to distinguish between valid and fraudulent transactions thanks to careful feature engineering and anomaly detection methods. Extensive experiments on a large and diverse collection of real and simulated credit card transactions, both legitimate and fraudulent, prove the success of this technique. The findings demonstrate state-of-the-art performance in fraud detection, with increased precision and recall rates compared to traditional methods. And because the presented ML models are easy to understand, they improve fraud risk management and prevention techniques. The findings of this study provide banking institutions, government agencies, and policymakers with vital information for combating the negative effects of credit card fraud on consumers, companies, and the economy as a whole. This study provides a solution to the problem of fraud in the Internet of Things (IoT) ecosystem and paves the way for future developments in this crucial area by proposing a unique ML-driven approach to the problem.