Credit Card Fraud Detection Using Machine Learning

The number of customers for the credit cards (CC) has grown of the last decade. These cards have aggravated the cashless systems of payments as well as alleviated the use of cash credit, which is termed as a short-term continuous loan. The credit cards are known to increase the purchasing power of citizens, and let them meet their daily needs, gadgets, etc. The number of CC (credit card) frauds has increased with the increase in number of credit cards. The unethical use of credit cards by hackers or credit cards users unwilling to pay back the amount are known as the major credit card frauds. The credit card frauds can be detected by evaluating the CC purchasing patterns using the historical data in order to detect the frauds. This data evaluation can help the banks or other organizations offering credit cards to minimize their losses due to the credit card frauds. The historical data evaluation with the current purchasing patterns requires the statistical modelling, which can automatically evaluate the fraudulent patterns and alarm the banks about the transactions. This helps the banks for early detection of the frauds, where they can easily eliminate the CC frauds by declining the suspected transactions.