Intelligent Credit Scoring. Siddiqi Naeem
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СКАЧАТЬ owned. This would mean customer’s data from their mortgage, credit card, auto loan, savings and checking accounts, and ATM usage would all be in the same place. Later in the book, we will refer to using such variables in scorecard development. In some banks this is known as the enterprise data warehouse (EDW).

      In any analytics infrastructure project, this is typically the most difficult and lengthiest phase. Organizations have dirty data, disparate data on dozens and sometimes hundreds of databases with no matching keys, incomplete and missing data, and in some cases coded data that cannot be interpreted. But this is the most important phase of the project, and fixing it has the biggest long-term positive impact on the whole process. Without clean, trusted data, everything else happening downstream is less valuable. We recognize, however, that waiting for perfectly matched clean data for all products before starting scorecard development, especially in large banks with many legacy systems, is not realistic. There is a reason EDW is known as “endless data warehouse” in far too many places. In order to get “quick hits,” organizations often take silo approaches and fill the data warehouse with information on one product, and then build and deploy scorecards for that product. They then move on to the next set of products in a sequential manner. This helps in showing some benefit from the data warehouse in the short term and is a better approach than waiting for all data for all products to be loaded in.

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      1

       www.sas.com/en_us/industry/banking/credit-scoring.html

      2

      Basel Committee for Banking Supervision, Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework, Bank for International Settlements, November 2005.

      3

      European Banking Federation, Study on Internal Rating Based (IRB) models in Europe, 2014.

      4

      L. Einav, M. Jenkins, J. Levin, ”The Impact of Credit Scoring on Consumer Lending,” RAND Journal of Economics

1

www.sas.com/en_us/industry/banking/credit-scoring.html

2

Basel Committee for Banking Supervision, Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework, Bank for International Settlements, November 2005.

3

European Banking Federation, Study on Internal Rating Based (IRB) models in Europe, 2014.

4

L. Einav, M. Jenkins, J. Levin, ”The Impact of Credit Scoring on Consumer Lending,” RAND Journal of Economics, 44, no. 2, (Summer 2013): 249–274.

5

Reg. B, 12 C.F.R. § 202.2(p)(2)(iii)(1978)

6

http://time.com/money/3978575/credit-scores-auto-insurance-rates/

7

www.cbc.ca/news/credit-scores-can-hike-home-insurance-rates-1.890442

8

Jane Dokko, Geng Li, and Jessica Hayes, “Credit Scores and Committed Relationships,” Finance and Economics Discussion Series 2015-081. Washington, DC: Board of Governors of the Federal Reserve System, 2015; http://dx.doi.org/10.17016/FEDS.2015.081

9

www.wsj.com/articles/silicon-valley-gives-fico-low-score-1452556468

10

Basel Committee on Banking Supervision document, BCBS 239, Principles for Effective Risk Data Aggregation and Reporting, Bank for International Settlements, January 2013.

11

www.forbes.com/sites/stevedenning/2011/11/22/5086/#c333bf95b560

12

Supervisory Guidance on Model Risk Management, Federal Reserve Bank, www.federalreserve.gov/bankinforeg/srletters/sr1107a1.pdf

13

Bank of International Settlements, Working Paper No. 14, 2005.

14

https://www.justice.gov/crt/equal-credit-opportunity-act-3

15

https://www.justice.gov/crt/fair-housing-act-2

16

https://www.congress.gov/bill/111th-congress/house-bill/4173

17

https://www.federalreserve.gov/boarddocs/supmanual/cch/fair_lend_reg_b.pdf

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