Enhancing Transaction Security: ML-Based Credit Card Fraud Detection

Abstract

The identification of credit card fraud is a crucial financial security tool that shields people and companies from illegal activities that might result in losses. The sophistication of fraudulent actions has increased over time, therefore financial institutions must use cutting-edge methods to analyze transaction patterns, spot irregularities, and stop fraud in real-time. Because of the growing number of online transactions, fraudsters are always coming up with new ways to take advantage of weaknesses, which means that fraud detection systems need to be updated and improved regularly. Thorough data collection is the first step in the fraud detection process. This includes information on transactions such as time, place, money, transaction data, and payment methods. In addition, fraud analysis requires contexts of user activity models such as cost trends, devices, IP addresses, and geographic position monitoring. To create a predictive model that distinguishes between true and fraudulent transactions, financial institutions often use past data for fraud. The next crucial stage after data collection is feature engineering, which entails transforming unprocessed data into useful features that increase the precision of fraud detection.