FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE  LEARNING TECHNIQUES

Aruna R; Revathi M

FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES

March 2024

Vol-10, Issue-2

Paper ID: 22964

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Machine learning techniques Supervised learning Model evaluation Digital transaction real time fraud detection Bagged Decision Tree Model Fraud Detection System Random Forest Classifier Synthetic Minority Over-sampling Technique Area Under the Curve Generative Adversarial Networks K-Nearest Neighbors Interquartile Range Receiver Operator Characteristic Application Programming Interface.

Abstract

Online payment fraud poses a significant threat to the integrity of digital transactions, leading to substantial financial losses for businesses and individuals. Traditional rule-based systems often fall short in detecting sophisticated fraudulent activities. In response, machine learning (ML) techniques have emerged as powerful tools for fraud detection by analyzing vast amounts of transactional data to identify patterns indicative of fraudulent behavior. This report explores the application of machine learning techniques in the realm of fraud detection in online payments, highlighting their advantages, challenges, and future directions. Key topics include the challenges in fraud detection, various machine learning techniques employed, the importance of feature engineering, model evaluation metrics, and future directions for enhancing fraud detection systems. Real-time implementation of these models within the payment processing pipeline ensures swift detection and response to potential fraud instances, bolstering the security and trustworthiness of online payment transactions. Through meticulous performance analysis and iterative refinement, the proposed system aims to deliver a scalable, accurate, and adaptive solution. We examine the effectiveness of three distinct machine learning models in terms of classification, prediction, and detection of fraudulent credit card transactions: logistic regression, random forest, and decision trees. As a result, we suggest that the best machine learning method for identifying and forecasting payment fraud is random forest. This study proposes an advanced fraud detection system designed to accurately identify and thwart fraudulent transactions while minimizing false positives and negatives.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Aruna R	Bannari amman institute of technology
2	Revathi M	Bannari amman institute of technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

R, Aruna & M, Revathi (2024). FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES. International Journal of Advance Research and Innovative Ideas In Education, 10(2), 1640-1646.

MLA Style

R, Aruna, and Revathi M. "FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, 2024, pp. 1640-1646.

IEEE Style

Aruna R and Revathi M, "FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, pp. 1640-1646, 2024.

Vancouver Style

R Aruna, M Revathi. FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(2):1640-1646.

Harvard Style

R, Aruna & M, Revathi (2024) 'FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES', International Journal of Advance Research and Innovative Ideas In Education, 10(2), pp. 1640-1646.

Chicago Style

R, Aruna and Revathi M. "FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 1640-1646.

Turabian Style

R, Aruna and Revathi M. "FRAUD DETECTION IN ONLINE PAYMENT USING MACHINE LEARNING TECHNIQUES." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 1640-1646.