Application Areas of Data Mining in Indian Retail Banking Sector

Application Areas of Data Mining in Indian Retail Banking Sector

Sudhakar M

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Dr. C. V. K Reddy

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α Sri Krishnadevaraya University

Application Areas of Data Mining in Indian Retail Banking Sector

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Abstract

Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details, risk profiles, credit card details, credit 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.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Sudhakar M. 2014. \u201cApplication Areas of Data Mining in Indian Retail Banking Sector\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C5): .

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GJCST Volume 14 Issue C5
Pg. 11- 17

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

AML Banking customer classification Data mining default detection unstructured data

Submission ReceivedDecember 8, 2013
Peer Review Double Blind
Handling Editor
Accepted January 5, 2014
Published January 15, 2014

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July 28, 2014

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Sudhakar M, Dr. C. V. K Reddy (PhD/Dr. count: 1)

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Total Views: 8849

Total Downloads: 2320

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