Review Paper on Reducing Fraud Detection System using Blockchain and ML

Review Paper on Reducing Fraud Detection System using Blockchain and ML

Pooja Patel (0225CS20MT12)*

*1(Department of CSE, Global Nature Care Sangathan's Group of Institutions, Jabalpur)

Abstract: In this paper, we address the problems of fraud and anomalies in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. A security analysis of the proposed smart contract is also performed to show the robustness of our system.

Keywords: anomaly detection, blockchain, fraud detection, machine learning, random forest.