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Credit Scoring Process using Banking Detailed Data Store

by Meera Rajan, Tulasi .b
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 6
Year of Publication: 2015
Authors: Meera Rajan, Tulasi .b
10.5120/ijais15-451332

Meera Rajan, Tulasi .b . Credit Scoring Process using Banking Detailed Data Store. International Journal of Applied Information Systems. 8, 6 ( April 2015), 13-20. DOI=10.5120/ijais15-451332

@article{ 10.5120/ijais15-451332,
author = { Meera Rajan, Tulasi .b },
title = { Credit Scoring Process using Banking Detailed Data Store },
journal = { International Journal of Applied Information Systems },
issue_date = { April 2015 },
volume = { 8 },
number = { 6 },
month = { April },
year = { 2015 },
issn = { 2249-0868 },
pages = { 13-20 },
numpages = {9},
url = { https://www.ijais.org/archives/volume8/number6/732-1332/ },
doi = { 10.5120/ijais15-451332 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:59:10.884942+05:30
%A Meera Rajan
%A Tulasi .b
%T Credit Scoring Process using Banking Detailed Data Store
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 8
%N 6
%P 13-20
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Credit scoring process has become the current popular need of the sectors like Banking, Telecom, and Insurance. The current paper discusses credit scoring for banking Sector. It discusses about Credit Scoring for BASEL II, also to build an integrated solution for it. The framework of credit scoring solution is to enable a bank to build Analytic models for application score or Probability of Default (PD),Loss Given default(LGD), Credit Conversion Factor (CCF). The credit scoring process is integrated with the Credit Risk Management. In this paper the SAS tool named SAS E-Miner is used to perform Credit Scoring using DDS (Detailed Data Store) and SEMMA methodology is applied.

References
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  4. "Creating Interval Target Scorecards with Credit Scoring for SAS® Enterprise Miner™". Miguel Maldonado, Susan Haller, Wendy Czika, and Naeem Siddiqi SAS Institute Inc. , Paper 094-2013
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  8. "Default Predictors in Retail Credit Scoring : Evidence from Czech Banking Data",Evžen Ko?enda , Martin Vojtek, Emerging Markets Finance & Trade, Vol. 47, No. 6 (November-December 2011), pp. 80-98, www. jstor. org/stable/41343442
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  10. Development of Credit Scoring Applications using SAS Enterprise Miner-User Guide
Index Terms

Computer Science
Information Sciences

Keywords

Credit Scoring Logistic Regression SEMMA Detailed Data Store SAS E-miner

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