Credit Card Fraud Detection in the Banking Sector: A Comprehensive Machine Learning Approach for Information Security

Abstract

In the context of computers, cybersecurity is experiencing significant technological development, and the changes have been driven by its operations in recent years. The secret to creating an intelligent and automated security system is to extract patterns or insights from cybersecurity data and create a matching data-driven model. One of the main problems, and threats to information security, is fraud. Credit card fraud detection (CCFD) is a significant issue for consumers, businesses, and banks, mostly because of the growth of computerized financial transactions. Because of this, a methodology for detecting fraud is presented that uses state-of-the-art machine learning (ML) techniques. The methodology in this research is a carefully chosen set of state-of-the-art ML algorithms that are particularly made for accurate CCFD problems. The technique uses a wide range of ML models to handle large-scale problems with a large number of transactions. Three ML models are used in this study, such as logistic regression (LR), random forest (RF), and XGBoost. These models are trained for accurate results of CCFD. Four evaluation metrics are used in this study to evaluate the ML models, such as accuracy, precision, recall, and f1 score. The results show that the RF model has the highest accuracy of 99.65%, followed by the XGBoost, with 99.963% accuracy, and the LR model, with 99.934% accuracy. The study's summary gives banking organizations, governmental organizations, and legislators crucial knowledge to help them fight against the harm that credit card theft does to customers, businesses, and the economy at large. By offering an ML-driven solution to the fraud problem, our work solves it and opens the door for further advancements in this important field.