RESEARCHING ALGORITHMS FOR CREDIT ALLOCATION USING DEEP LEARNING METHODS

O. Bozorov

doi:10.5281/zenodo.7391046

O. Bozorov

Abstract. This article discusses the main methods of credit placement. A credit rating involves the use of algorithms obtained by mathematical and statistical methods to separate potential credit transactions into mismatched risk groups. This article describes the advantages and limitations of various models and algorithms used in the distribution of credit, as well as the prospects for further development of this method of assessing credit risk.

Keywords: Credit rating, scoring models, credit risks, decision trees, neural networks

References:

1. Berger A.N., Frame W.S. Small Business Credit Scoring and Credit Availability // Journal of Small Business Management. 2007. Vol.45. No.1. Pp. 5-22. 2. Demyanyk Y. Your Credit Score Is a Ranking, Not a Score // Economic Commentary. 2010. №2010-16. URL: https://www.clevelandfed. org/en/newsroom-andevents/publications/economic-commentary/ economic-commentary-archives/2010-economiccommentaries/ec201016-your-credit-score-is-a-ranking-not-a-score.aspx. 3. Hand D.J., Henley W.E. Statistical Classification Methods in Consumer Credit Scoring: a Review // Journal of the Royal Statistical Society: Series A (Statistics in Society). 1997. Vol.160. No.3. Pp. 523-541. 4. Huang C.L., Chen M.C., Wang C.J. Credit Scoring with a Data Mining Approach Based on Support Vector Machines // Expert systems with applications. 2007. Vol.33. No.4. Pp. 847-856. 5. Martens D. et al. Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines // European Journal of Operational Research. 2007. Vol.183. No.3. Pp. 1466-1476. 6. Mester L.J. et al. What’s the Point of Credit Scoring? // Business review. 1997. Vol.3. No. Sep/Oct. Pp. 3-16.