Credit Card Fraud Detection System to Enhance Legitimate Transactions Using Artificial Neural Networks

Abstract

The explosive expansion of E-Commerce has led to a corresponding surge in credit card fraud as more people use their cards to make transactions online. Credit card fraud is on the rise as credit cards become the preferred method of payment for both online and in-store purchases. In practice, fraudulent and legitimate transactions coexist, and basic pattern-matching approaches are typically insufficient to identify fraud effectively. While Artificial Neural Networks (ANNs) offer several advantages, it's important to note that they require careful tuning, validation, and ongoing monitoring to ensure optimal performance. Additionally, the interpretability of ANNs can be a challenge, and efforts are being made to enhance model interpretability in the context of fraud detection. Identifying fraudulent transactions while avoiding false positives is the main objective of a credit card fraud detection system that uses ANNs effectively and correctly. Therefore, for credit card issues to reduce their loss, it is crucial that they implement reliable fraud detection systems. Various forms of credit card fraud may now be detected using cutting-edge methods based on Artificial Intelligence (AI), Data mining, Fuzzy logic, Machine learning (ML), sequence alignments, Genetics Programming, etc. An effective system for detecting credit card fraud requires a thorough grasp of all these methods. Using a set of predetermined criteria, this study examines the various neural network techniques currently in use for credit card fraud detection and provides an overall rating for each. The result shows 98% accuracy and 96% recall for the credit card fraud detection system.