2022, issue 3, p. 23-36

Received 21.10.2022; Revised 05.11.2022; Accepted 15.11.2022

Published 29.11.2022; First Online 10.12.2022

https://doi.org/10.34229/2707-451X.22.3.3

Previous  |  FULL TEXT (in Ukrainian)  |  Next

 

MSC 91B82, 90C05, 90C10, 92B20

Statistical and Optimization Methods in Credit Scoring

Viktor Stovba ORCID ID favicon Big

V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv

Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Introduction. The role of credit scoring in the work of financial institutions is difficult to overestimate. Accurate and efficient scorecards allow lenders to assess risks correctly and monitor their investments. Such cards should be based on reliable statistical data about previous and current customers using statistical analysis methods.

Over the years of its development, the toolkit of credit scoring has also been supplemented with non-statistical methods based on the use of optimization procedures, decision trees, intelligent databases and knowledge bases, building network models, etc. Given the wide range of available methods, there is a need for their classification and application analysis.

The purpose of the article is to provide a brief description of all relevant statistical and non-statistical methods that allow solving credit scoring tasks in modern formulations. To reveal the features of using the methods described and conduct their comparison.

Results. Statistical methods allow to investigate the significance of all the factors included in the model, as well as to obtain a set of statistical estimates that help to assess the quality of the model. Thus, these methods allow to build an optimal and reliable model. Non-statistical methods allow you to add arbitrary restrictions to the model, automatically detect and process interactions between characteristics, and solve problems with a large number of applicants and their characteristics, which is facilitated due to the development of computational methods.

Conclusions. Modern mathematical methods allow to solve credit scoring tasks effectively, among which one of the main ones is the binary and multigroup classification. The choice of the optimal method depends on the type of system (static or dynamic), the creditor's computing capabilities and the importance of the results interpretation.

 

Keywords: credit scoring, statistical methods, mathematical programming, neural networks, genetic algorithms.

 

Cite as: Stovba V. Statistical and Optimization Methods in Credit Scoring. Cybernetics and Computer Technologies. 2022. 3. P. 23–36. (in Ukrainian) https://doi.org/10.34229/2707-451X.22.3.3

 

References

           1.     Lyn C. Thomas, David B. Edelman, Jonathan N. Crook. Credit Scoring and its Applications. SIAM Monographs on Mathematical Modeling and Computation. Philadelphia, 2002. 243 p. https://doi.org/10.1137/1.9780898718317

           2.     Sarlija N., Bensic M., Bohacek Z. Multinomial Model in Consumer Credit Scoring. 10th International Conference on Operational Research. Trogir: Croatia. 2004.

           3.     Abdou H., Pointon J. Credit scoring and decision-making in Egyptian public sector banks. International Journal of Managerial Finance. 2009. Vol. 5. N 4. P. 391–406. https://doi.org/10.1108/17439130910987549

           4.     Abdou H., Pointon J., El Masry A. Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications. 2008. Vol. 35. N 3. P. 1275–1292. https://doi.org/10.1016/j.eswa.2007.08.030

           5.     Baesens B., Gestel T.V., Viaene S., Stepanova M., Suykens J., Vanthienen J. Benchmarking State-of-the-Art Classification Algorithms for Credit Scoring. Journal of the Operational Research Society. 2003. Vol. 54. N 6. P. 627–635. https://doi.org/10.1057/palgrave.jors.2601545

           6.     Yurynets Z., Yurynets R., Kunanets N., Myshchyshyn І. Regression model of assessment of customer solvency and banking risks in the process of lending. Socio-Economic Problems of the Modern Period of Ukraine. 2019. Vol. 138. N 4. P. 69–73. (in Ukrainian) https://doi.org/10.36818/2071-4653-2019-4-11

           7.     Mangasarian O.L. Linear and nonlinear separation of patterns by linear programming. Oper. Res. 1965. Vol. 13. P. 444–452. https://doi.org/10.1287/opre.13.3.444

           8.     Nath R., Jones T.W. A variable selection criterion in the linear programming approaches to discriminant analysis. Decision Sci. 1988. Vol. 19. P. 554–563. https://doi.org/10.1111/j.1540-5915.1988.tb00286.x

           9.     Gately E. Neural Networks for Financial Forecasting: Top Techniques for Designing and Applying the Latest Trading Systems. New York: John Wiley & Sons, Inc. 1996.

       10.     Velykoivanenko G.І., Savina S.S., Kolechko D.V., Ben V.P. Construction of ensembles of credit scoring models. Neuro-fuzzy modeling technologies in economics. 2018. Vol. 7. P. 3477. (in Ukrainian) https://www.doi.org/10.33111/nfmte.2018.034/

       11.     Zekic-Susac M., Sarlija N., Bensic M. Small Business Credit Scoring: A Comparison of Logistic Regression, Neural Networks, and Decision Tree Models. 26th International Conference on Information Technology Interfaces. Croatia. 2004.

       12.     Huang J., Tzeng G., Ong C. Two-stage genetic programming (2SGP) for the credit scoring model. Applied Mathematics and Computation. 2006. Vol. 174. N 2. P. 1039–1053. https://doi.org/10.1016/j.amc.2005.05.027

       13.     Huang C., Chen M., Wang C. Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications. 2007. Vol. 33. N 4. P. 847–856. https://doi.org/10.1016/j.eswa.2006.07.007

       14.     Etemadi H., Rostamy A., Dehkordi H. A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications. 2009. Vol. 36. N 2/2. P. 3199–3207. https://doi.org/10.1016/j.eswa.2008.01.012

       15.     McKee T., Lensberg T. Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research. 2002. Vol. 138. N 2. P. 436–451. https://doi.org/10.1016/S0377-2217(01)00130-8

       16.     Talebzadeh H., Mandutianu S., Winner C. Countrywide loan underwriting expert system. In: Proceedings of the Sixth Innovative Applications of Artificial Intelligence Conference. AAAI Press, Menlo Park, CA. 1994. https://doi.org/10.1609/aimag.v16i1.1123

       17.     Ben-David A., Frank E. Accuracy of machine learning models versus “hand crafted” expert systems – a credit scoring case study. Expert Systems with Applications. 2009. Vol. 36. N 3/1. P. 5264–5271. https://doi.org/10.1016/j.eswa.2008.06.071

       18.     Kumra R., Stein R., Assersohn I. Assessing a knowledge-based approach to commercial loan underwriting. Expert Systems with Applications. 2006. Vol. 30. N 3. P. 507–518. https://doi.org/10.1016/j.eswa.2005.10.013

 

 

ISSN 2707-451X (Online)

ISSN 2707-4501 (Print)

Previous  |  FULL TEXT (in Ukrainian)  |  Next

 

 

 

© Website and Design. 2019-2024,

V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine,

National Academy of Sciences of Ukraine.