CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Abstract

The project "Credit Card Fraud Detection with FBA" aims to develop a robust and efficient system for identifying and preventing fraudulent activities associated with credit card transactions within the context of Fulfilment by Amazon (FBA). With the increasing prevalence of online transactions, credit card fraud has become a significant concern for both consumers and businesses. FBA, being a prominent e-commerce platform, requires a sophisticated fraud detection mechanism to ensure secure and trustworthy transactions. The project utilizes advanced machine learning algorithms to analyse transaction data and detect patterns indicative of potential fraud. These algorithms are trained on historical data, enhancing their ability to recognize anomalies in real-time transactions. The system incorporates a multi- layered approach, utilizing address verification, device fingerprinting, and IP geolocation to validate the legitimacy of transactions and minimize false positives. Real-time monitoring is a key component of the system, allowing for immediate detection and response to suspicious activities. Manual review processes are integrated, involving trained personnel to investigate flagged transactions and make informed decisions based on additional information or customer verification. This human element adds an extra layer of scrutiny, contributing to the reliability of the fraud detection system. The project aims to create a dynamic and adaptive fraud detection system that evolves wit emerging fraud tactics. Regular updates and improvements will be implemented to address new challenges and enhance the overall security of credit card transactions within the FBA ecosystem. The successful implementation of this project will contribute to a safer and more secure online shopping experience for FBA users, fostering trust and confidence in the platform. Index terms: Credit card fraud detection, Fulfilment by Amazon (FBA), Machine learning algorithms, Fraudulent activities, Online transactions, Real-time monitoring, Multi-layered approach, Address verification, Device fingerprinting, IP geolocation, Manual review processes, Dynamic and adaptive system, Emerging fraud tactics, Security enhancements, Online shopping experience, Trust and confidence building