A Review Paper on Credit Card Fraud Detection Using Machine Learning

A Review Paper on Credit Card Fraud Detection Using Machine Learning

Asst.Prof Anush Sharma

Sahil, Anish Kumar, Simran

Himachal institute of engineering and technology,

Shahpur , Himachal Pradesh

INDIA

Abstract - The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented.Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly.Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.

Keywords       Introduction, Machine Learning ,Methodology, Linear Regression, Analytics and Literature Review, Conclusion