Role of Machine Learning in Credit Card Fraud Detection

Abstract

Fraud is the occurrence of any activity done using misleading, deceptive, or illegal ways which is done by someone to defraud you of your money (or capital), or otherwise jeopardizes financial well-being of you or your company. Online fraud is defined as fraud done through the use of the internet. Identity theft or outright money fraud are both examples of online fraud. Fraud detection means a collection of activities to avoid the collecting of money/property by misleading pretensions. A number of sectors are today using fraud detection which includes ecommerce vendors and banking agencies. Due to rapid advancements within the digital commerce generation, the mode of payment has moved from cash to digital settlements such as debit/credit card, online wallet payment, and online banking, the market value of fraud detection and prevention is rising year over year. As a result, financial fraud is increasing at a rapid rate for personal gain. Banking/financial firms are developing fraud detection mechanisms to protect consumers from fraudulent transactions in response to this scenario. This paper explores different types of frauds, the limitations of existing detecting systems and the advantages of using machine learning for fraud detection.