Data mining in banking and its applications- A review

38Citations
Citations of this article
117Readers
Mendeley users who have this article in their library.

Abstract

Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details, risk profiles, credit card details, limit and collateral details, compliance and AntiMoney Laundering (AML) related information, trade finance data, SWIFT and telex messages. Thousands of decisions are taken in a bank daily. These decisions include credit decisions, defaultdecisions, relationship start up, investment decisions, AML and Illegal financing related. One needs todepend on various reports and drill down tools provided by the banking systems to arrive at these criticaldecisions. But this is a manual process and is error prone and time consuming due to large volume oftransactional and historical data. Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process. This article explores and reviewsvarious data mining techniques that can be applied in banking areas. It provides an overview of datamining techniques and procedures. It also provides an insight into how these techniques can be used inbanking areas to make the decision making process easier and productive. © 2013 Science Publications.

Cite

CITATION STYLE

APA

Pulakkazhy, S., & Balan, R. V. S. (2013). Data mining in banking and its applications- A review. Journal of Computer Science. https://doi.org/10.3844/jcssp.2013.1252.1259

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free