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 Anti Money 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, default decisions, relationship start up, investment decisions, AML and Illegal financing related. One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions. But this is a manual process and is error prone and time consuming due to large volume of transactional 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 reviews various data mining techniques that can be applied in banking areas. It provides an overview of data mining techniques and procedures. It also provides an insight into how these techniques can be used in banking areas to make the decision making process easier and productive.
Highlights
How, this mountain of data is turning out to be the most valuable asset of the organization (Tiwari, 2010)
One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions
This article explores and reviews various data mining techniques that can be applied in banking areas
Summary
How, this mountain of data is turning out to be the most valuable asset of the organization (Tiwari, 2010). Balan / Journal of Computer Science 9 (10): 1252-1259, 2013 process They may use drill down tools provided by the system for analyzing data to arrive at critical decisions. It could be possible that loan installments are being paid regularly though there is an alarming negative trend in the customers turnover and the account may be about to default. These associations are not easy to detect through manual processes. It is assumed that valuable information are hidden in this volume of operational and historic data that can be used for critical decision making process if they are discovered and put to use by capable tools (Kazi and Ahmed, 2012).
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