Smart Fraud Detection Leveraging Machine Learning For Credit Card Security

Abstract

Credit card fraud poses a significant threat to financial institutions and consumers worldwide. In this research, we propose a comprehensive machine learning framework for the detection of credit card fraud, encompassing data collection, preprocessing, model building, and evaluation. The dataset utilized, presenting a notably imbalanced distribution of fraudulent and non-fraudulent transactions. To address this imbalance, we employ techniques such as random undersampling and the Synthetic Minority Over-sampling Technique. The methodology includes an extensive Exploratory Data Analysis phase to uncover the data's underlying patterns and inform preprocessing steps, including data cleaning, feature scaling, and class balancing. We then construct and compare various machine learning models, notably Random Forest and Support Vector Machine (SVM), optimizing their performance through hyperparameter tuning and enhancing robustness via ensemble methods. The models are evaluated using metrics such as accuracy, precision, recall, and F1-score, with cross-validation techniques ensuring the generalizability of results. Empirical results demonstrate that the Random Forest model achieves superior performance with balanced precision and recall metrics, indicating effective fraud detection capabilities. Conversely, the SVM model, despite initially high accuracy, exhibits signs of overfitting, underscoring the necessity for robust model validation. Our findings highlight the critical need for continuous monitoring and adaptation of machine learning models to keep pace with evolving fraud tactics. This research provides essential insights for financial institutions seeking to deploy resilient and effective credit card fraud detection systems.