Design and Analysis of Advanced Machine Learning Methods for Financial Fraud Identification in Credit Card Activities

Abstract

Credit card theft is simple and easy to do. There are more ways to pay for things online, thanks to e-commerce and many other websites. This makes online scams more likely. The frequency of online transaction fraud prompted specialists to employ a toolbox of machine learning methods in their quest to uncover and analyze the problem. The risk of credit card scams has gone up a lot because of the fast growth of digital financial services like online shopping, online banking, and mobile payments. When it comes to complex fraud, traditional security measures like encryption and tokenization often fall short. Credit card theft can be better detected by comparing CNN and K-Nearest Neighbors (KNN), two machine learning models that were recommended in the study. The research employs the SMOTE-ENN resampling method to rectify the data discrepancy in a freely accessible dataset of extremely unequal cardholder transactions throughout Europe. Ensuring data quality is achieved through comprehensive preparation that incorporates Min Max scaling, categorical encoding, and imputation for missing values. Measures such as accuracy, precision, recall, F1-score, and AUPRC mostly concentrate on the model's ability to deal with class imbalance. On the basis of the lab data, the CNN model outperforms the KNN and baseline models with respect to accuracy rate (99.68%) and F1-score (99.60%). My last remarks: Through the application of deep learning techniques, develop scalable fraud detection systems capable of identifying tiny indicators of fraud in real-time financial transactions.