Paper Key : IRJ************950
Author: Vallepu Rupavathi,Tallapalli Penchala Reddy,Revelli Kotesh,Thirumala Shivakumar,N Jyothi
Date Published: 14 Nov 2024
Abstract
Fraudulent payment transactions pose significant challenges to financial institutions, often leading to substantial financial losses and undermining customer trust. Traditional methods for fraud detection rely on predefined rules and heuristics, which are often inadequate for adapting to evolving fraudulent strategies. This project proposes a Fraud Payment Transaction Detection System using Machine Learning, specifically leveraging the Random Forest algorithm. The proposed system aims to enhance the accuracy and robustness of fraud detection by analyzing historical transaction data to identify patterns indicative of fraudulent activities. The Random Forest algorithm, known for its high performance and accuracy in classification tasks, will be employed to improve the detection rate of fraudulent transactions. Preliminary results indicate that the Random Forest-based system achieves superior accuracy compared to conventional methods, thereby providing a more reliable solution for real-time fraud detection.
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