Fraud Detection in Credit Card Data using Unsupervised Machine Learning Based Scheme

Project Code :TCMAPY379

Objective

This paper proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Our method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering.

Abstract

Development of communication technologies and eCommerce has made the credit card as the most common technique of payment for both online and regular purchases. So, security in this system is highly expected to prevent fraud transactions. In this paper, researchers are also trying the novel techniques to detect and prevent such frauds. However, there is always a need of some techniques that should precisely and efficiently detect these frauds.

This project proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Proposed method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering.

Keywords: Unsupervised Learning, Anomaly Detection, Fraud Detection, Auto-Encoder, Credit Card.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.