Credit Card Fraud Detection

An enormous challenge for both financial institutions and consumers as a result of the exponential expansion in digital transactions and the related increase in credit card fraud incidences. As fraudulent actions get more complicated, traditional ways of detecting fraud are becoming less and less effective. Utilizing cutting-edge analytical methods is therefore imperative in order to safeguard financial assets and maintain public confidence in the banking system. To examine transaction data from credit card networks and financial institutions, we use machine learning methods in this study. In particular, we apply sentiment analysis to identify possible credit card fraud using the Random Forest, Logistic Regression, and Naive Bayes algorithms. These algorithms were selected because they work well with big datasets and can identify trends that point to fraudulent activity. When accuracy is used to evaluate algorithm performance, Naïve Bayes comes out on top, with an impressive accuracy percentage of 99.30% among the algorithms examined. Nonetheless, the Random Forest and Logistic Regression algorithms also perform admirably, with accuracy rates of 98.5% respectively.