Data Mining Techniques for Fraud Detection in Banking Sector

Banking sector is having a great significance or value in our everyday life. Each and every person makes the use of banking sector in two ways, (i) physical and (ii) online. Physical fraud can take place like stealing the credit cards, sharing bank account details with corrupt bank employees, etc. Online fraud takes place by sharing the card details on the Internet or over the phone with a wrong person. It may also include spamming and phishing. While carrying out the transactions and all the relations with the bank policies, customers and the banks may face many problems due to fraudsters and criminals, and the chances of getting trapped are very higher. These kinds of frauds can be credit card fraud, insurance fraud, accounting fraud, etc. which may lead to the financial loss to the bank or the customers. Thus, detection of these kinds of frauds are very important. Fraud detection in banking sector is based on the data mining techniques and their collective analysis from the past experiences and the probability of how the fraudsters can steal from customers and banks. Therefore this paper addresses the analysis of data mining techniques of how to detect frauds and overcoming it in banking sector.