Data mining in banking and its applications- A review

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.