A conceptual model for the use of artificial intelligence for credit card fraud detection in banks

Credit cards play a role in economic growth because they allow for a cashless society which in turn reduces government expenditure on the manufacturing and distribution of monetary notes. A cashless society would allow governments to save billions of money that can be ploughed back into the economy for other purposes. However, mediums of achieving a cashless society such as credit cards are under attack from fraudsters. Recent studies show that more and more money is being fraudulently withdrawn from accounts. This paper aims to evaluate the credit card fraud detection methods used by banks and the difficulties in implementing the said methods. The study suggests the use of artificial intelligence, geolocation and data mining in credit card fraud detection methods to mitigate the weaknesses that current credit card fraud detection methods have. The use of artificial intelligence, data mining and geolocation would enable credit card fraud detection methods to analyse and identify trends in customer spending to identify fraudulent transactions. A model is introduced to help mitigate the weaknesses. An indepth literature review was undertaken and secondary research was used throughout the study as the main source of information.