Credit Card Fraud Detection using Machine Learning and Data Mining Techniques - a Literature Survey

Abstract

Purpose: To understand the algorithms used in Credit Card Fraud Detection (CCFD) using Machine Learning (ML) and Data Mining (DM) techniques, Review key findings in the area and come up with research gaps or unresolved problem. To become knowledgeable about the current discussions in the area of ML and DM.

Design/Methodology/Approach: The survey on CCFD using ML and DM was conducted based on data from academic papers, web articles, conference proceedings, journals and other sources. Information is reviewed and analysed.

Results/Findings: Identification of credit card fraud is essential for protecting a person's or an organization's assets. Even though we have various safeguards in place to prevent fraudulent activity, con artists may develop a method to get around the checkpoints. We must create straightforward and efficient algorithms employing ML and DM to anticipate fraudulent activities in advance.

Originality/Value: Study of ML and DM algorithms in CCFD from diverse sources is done. This area needs study due to recent methods by fraudsters in digital crime have developed. The information acquired will be helpful for creating new methodologies or improving the outcomes of current algorithms.

Type of Paper: Literature Review.