MACHINE LEARNING BASED CREDIT CARD FRAUD ANALYSIS, MODELING, DETECTION AND DEPLOYMENT.

Credit card fraud is critical business risk that every stakeholder of financial system including issuer, acquirer etc. has to manage tightly to ensure business continuity and credibility of payment system. As the popularity of the credit card payment as one of the online payment mode is increasing more and more due to the revolution that has taken place in E-commerce sector. Traditional fraud models designed years back deliver near about 70% accuracy and were meeting business needs till this time. However fraudsters are increasing gaming the system to create new types of frauds which has resulted in consistent decrease in model accuracy. The fraudulent transactions and real transactions are scattered all around and there is very little difference to distinguish between them. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Sequence Alignment, Genetic Programming, Machine learning has evolved in detecting various credit card fraudulent transactions. This paper represents how the combinations of different clustering and machine learning algorithm which can best adapt to the changing scenarios taking place can be used and deployed on a very large scale to detect the fraudulent transactions and use to ensure the credibility of the payment system.