The Method of Construction Scoring Cards Using SAS Platform

Authors

  • Сабіна Антонівна Бакун ESC "Institute for applied system analysis" of the National Technical University of Ukraine "Kyiv Polytechnic Institute", Ukraine https://orcid.org/0000-0003-3773-2048
  • Петро Іванович Бідюк ESC "Institute for applied system analysis" of National Technical University of Ukraine "Kyiv Polytechnic Institute", Ukraine https://orcid.org/0000-0002-7421-3565

DOI:

https://doi.org/10.20535/1810-0546.2016.2.67487

Keywords:

Risk management, Data mining, Credit scoring, Scoring card, Logistic regression, Classification quality

Abstract

Background. Development of effective methods for evaluating solvency of individuals and risk of banks in providing consumer loans.

Objective. Determining of the mechanisms for implementation of scoring models in the form of scoring cards. Analysis of the possibility of using scoring cards as a tool for credit risk management.

Methods. Construction of scoring cards and preliminary analysis of input data using specialized component of the SAS Enterprise Miner.

Results. The main stages of scoring cards development were considered. The scoring card was constructed that is based on actual statistical data on granting of the consumer loans. The research also presents comparative analysis of the scoring cards with other statistical methods of subjects classification.

Conclusions. It was established in this study that the scoring cards have better forecasting ability than other statistical methods such as decision trees, neural networks and logistic regression. The format of development the forecasting models in the form of scoring cards is the easiest for interpreting. However, application of this method requires considerable investments as well as continuous updating and renewal of credit histories for borrowers.

Author Biographies

Сабіна Антонівна Бакун, ESC "Institute for applied system analysis" of the National Technical University of Ukraine "Kyiv Polytechnic Institute"

Sabina A. Bakun, student

Петро Іванович Бідюк, ESC "Institute for applied system analysis" of National Technical University of Ukraine "Kyiv Polytechnic Institute"

Petro I. Bidyuk, doctor of technical sciences, professor

References

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G. Xuesong et al., “Corporate credit rating model using support vector domain combined with fuzzy clustering algorithm”, Math. Problems Eng., vol. 1, 2012, pp. 1–20.

T. Lunkina. (2015). Using Scoring Models in Consumer Lending Risk Management [Online]. Available: http://www.economy. nayka.com.ua/?op=1&z=3792 (in Ukrainian).

A. Sorokin. (2014). Building a Scorecard Using a Logistic Regression Model. [Online]. Available: http://naukovedenie.ru/PDF/ 180EVN214.pdf (in Russian).

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A. Sorokin. (2014). On the Question of Validation of a Logistic Regression Model in Credit Scoring. [Online]. Available: http://naukovedenie.ru/PDF/ 173EVN214.pdf (in Russian).

A. Terentyev et al., SAS BASE: Programming Basics.Kyiv,Ukraine: Edelveis Publishers, 2015 (in Russian).

B.S. Anderson and R.W. Thompson, Developing Credit Scorecards Using SAS Credit Scoring for Enterprise Miner 5.3. Cary: SAS Institute Inc, 2009.

Published

2016-05-17

Issue

Section

Art