CFP last date
17 June 2024
Reseach Article

Credit Scoring Process using Banking Detailed Data Store

by Meera Rajan, Tulasi .b
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
  1. "Technical paper: Optimize ETL for the Banking DDS" http://support. sas. com/documentation/onlinedoc/dds/
  2. "SAS Detail Store for Banking: Implementation and User Guide- 2. 0 to 4. 8", http://support. sas. com/documentation/onlinedoc/dds/ddsadmin32. pdf
  3. "Pervasive SAS Techniques for Designing a Data warehouse for an Integrated Enterprise : An Approach towards Business process ",Ardhendu Tripathy et al,/(IJCSIT) International Journal of Computer Science and Information Technologies, vol. 2(2), 2011,853-861Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  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
  5. "Building Loss Given Default Scorecard Using Weight of Evidence Bins in SAS® Enterprise Miner™", Anthony Van Berkel, Bank of Montreal and Naeem Siddiqi, SAS Institute Inc. ", Paper 141-2012.
  6. "Risk and Compliance in Banking: Data Management Best Practices", white paper, www. sas. com/resources/ white paper. http://www. sas. com/resources/whitepaper/wp_65853. pdf
  7. "A Comparative Study of Data Mining Techniques for Credit Scoring in Banking",Shin-Chen Huang, Min-Yuh Day, Department of Information Management, Tamkang University, Taiwan DOI: 10. 1109/IRI. 2013. 6642534 Publication Year: 2013 , Page(s): 684 – 691, IEEE Conference Publications
  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
  9. "Credit scoring for individuals", Maria DIMITRIU, Elena Alexandra AVRAMESCU, Razvan Constantin CARACOTA: Editura ASE: 2010 December : Economia : Seria Management, Vol 13, Iss 2, Pp 361-377 (2010): 1454-0320.
  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