Harnessing Machine Learning to Detect and Prevent Credit Card Fraud

This study investigates the utilization of machine learning algorithms to detect fraudulent credit card transactions in real-time with a dataset that includes transaction amounts, timestamps and other anonymized features. This research compares different models in terms of performance to catch fraud transactions, such as logistic regression, random forest. Looking ahead, it is observed in the analysis that some specific anonymized features, especially 'V17', 'V14', and 'V10', become important to predict fraudulent activity. Random Forest was the top-performing model, with better accuracy and precision and a sharp decrease in false positives. The study highlights the necessity of feature selection and data preprocessing that can improve model performance on such problems. This research is able to shed light on different ways that fraud detection systems can be made more effective in the financial sector. Implementing machine learning models with these systems is also capable of making a huge improvement in fraud prevention, which could provide more secure transactions and reduce financial loss significantly. Furthermore, the increased accuracy and speed of these models help increase customer confidence and satisfaction in financial institutions, strengthening them against advancing fraud methods. We are demonstrating that our system can enable the next generation of more accurate and much larger-scale AV fraud detection tools to be implemented based on current real-time data provided by the multi-ETL cluster. Previously, OMV machines were known for their high scalability.