Integrating Machine Learning and SMOTE Framework: An Effective Credit Card Fraud Detection System

Credit card fraud is a risk that threatens financial systems because it leads to high monetary losses and hampers consumer’s faith in the systems. The greater the time lag in reporting a transaction that is deemed malicious, the more damage is done. This research investigates the use of machine learning algorithms, such as the Random Forest Classifier, in the development of predictive models for the detection of credit card fraud. The model improves on previous attempts through the combination of secured datasets of anonymized transaction records and a variety of features such as average transaction volume and frequency patterns. The model was built and tested in a dataset that had unbalanced classes where the number of money-related transactions was significantly greater than that of fraudulent transactions. The model’s predictive power was boosted by incorporating synthetic oversampling through SMOTE and other advanced preprocessing methods with AUC-ROC statistics of 0.95 being recorded, indicating strong performance of accurately distinguishing between genuine and distorted transactions. In terms of transaction risk, feature importance determination showed that user behavior patterns, transaction amount, and transaction time were ranked highest out of the others. Random Forest models can perform better than earlier technologies in terms of fraud management systems because they perform the two in a better way. The constructed framework enables financial institutions to allocate resources and place emphasis on investigations and preventive measures where they are most needed. The proposed model is further substantiated by experimental results, which present an accuracy of 0.93 and an F1 score of 0.93.