Fraud Detection Using Machine Learning and Blockchain

Abstract

In the 21st century financial fraud is on the rise in many institutions. Newly released Federal Trade Commission data shows that consumers reported losing nearly $8.8 billion to fraud in 2022, an increase of more than 30 percent over the previous year. The main goal for us is to develop an efficient fraud detection system and utilize blockchain to create a decentralized banking application. Our team has collaborated on various cutting-edge technologies, such as machine learning and blockchain, to create a sophisticated fraud detection system. We have implemented three machine learning algorithms, namely Logistic Regression, Decision Tree, and Random Forest, which have been used to improve the accuracy of the model for detecting fraudulent activities. As for fraud aversion, we have used the Blockchain technology which provides a tamper-proof system that can securely record and track financial transactions, ensuring transparency and security. This feature makes it an ideal solution for fraud prevention, as it guarantees that all transactions are legitimate and free from any manipulations. By combining the power of machine learning and blockchain technology, our team is confident in providing an innovative solution that will benefit all stakeholders involved