International Journal of Social and Economic Research

  • Year: 2019
  • Volume: 9
  • Issue: 3

Data mining techniques applied in banking sector- A review

  • Author:
  • Deepali Kamath1, K Pavithra2, Kavita Pujari3
  • Total Page Count: 8
  • Published Online: Sep 1, 2019
  • Page Number: 358 to 365

1Department of Computer Science, MGM College, Udupi, India

2Department of Computer Science, MGM College, Udupi, India

3Department of Computer Science, MGM College, Udupi, India

Abstract

In banking sector, Data mining is becoming essential, which examines the data methodically and in detail from various aspects and synopsize it. Data mining facilitates the bank to find out hidden relationship in the data. In current business world, Customer retention is more valuable than customer attrition. Fraud is another substantial problem in banking. Fraud prevention is better than fraud detection but since fraudsters are telepathic, every new scheme will go in vain. The main goal in banking is to provide security for the customer data. In order to keep the transactions safe and secure the undertaking gives a thought of how data mining abilities can give the expanded customer maintenance and minimizing the risk. This paper contains a widespread review of data mining techniques applied on banking sector. This paper analyses the data mining techniques like association and classification methods and their applications in two main areas such as fraud detection and customer relationship management (CRM) of banking sector.

Keywords

Fraud detection, Customer relationship management (CRM), Association, Classification