زمان واقعی تشخیص کلاهبرداری کارت اعتباری با استفاده از هوش محاسباتی

Online banking and e-commerce have been experiencing rapid growth over the past few years and show tremendous promise of growth even in the future. This has made it easier for fraudsters to indulge in new and abstruse ways of committing credit card fraud over the Internet. This paper focuses on real-time fraud detection and presents a new and innovative approach in understanding spending patterns to decipher potential fraud cases. It makes use of self-organization map to decipher, filter and analyze customer behavior for detection of fraud.

The fast and wide reach of the Internet has made it one of the major selling channels for the retail sector. In the last few years, there has been a rapid increase in the number of card issuers, card users and online merchants, giving very little time for technology to catch-up and prevent online fraud completely. Statistics shows that on-line banking has been the fastest growing Internet activity with nearly 44% of the population in the US actively participating in it (Fighting Fraud on the Internet, 1999). As overall e-commerce volumes continued to grow over the past few years, the figure of losses to Internet merchants was projected to be between $5 and $15 billion in the year 2005. Recent statistics by Garner group place online fraud rate between 0.8% and 0.9%, with auction fraud accounting to nearly half of the total incidents of fraud on the Internet (Online Banking, 2005). Considering the current trends of e-commerce volumes, the projected loss is $8.2 billion in the year 2006, with $3.0 billion in the US alone (Statistics for General & Online Fraud, 2007). 1.1. How and where does fraud begin In order to understand the severity of credit card fraud, let us briefly look into the mechanisms adopted by fraudsters to commit fraud. Credit card fraud involves illegal use of card or card information without the knowledge of the owner and hence is an act of criminal deception. Fraudsters usually get hold of card information in a variety of ways: Intercepting of mails containing newly issued cards, copying and replicating of card information through skimmers or gathering sensitive information through phishing (cloned websites) or from unethical employees of credit card companies. Phishing involves acquiring of sensitive information like card numbers and passwords by masquerading as a trustworthy person or business in an electronic communication such as e-mail (Schneck, 2007). Fraudsters may also resort to generation of credit card numbers using BIN (Bank Identification Numbers) of banks. A recent scheme of Triangulation takes fraud fighters many days to realize and investigate ( Bhatla, Prabhu, & Dua, 2003). In this method, the fraudster operates through an authentic-looking website, where he advertises and sells goods at highly discounted prices. The unaware buyer submits his card information and buys goods. The fraudster then places an order with a genuine merchant using the stolen card information. He then uses the stolen card to purchase other goods or route funds into intractable accounts. Its only after several days that the merchant and card owners realize about the fraud. This type of fraud causes initial confusion that provides camouflage for the fraudster to carry out their operations.