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Finance and Credit
 

Evaluation of small business creditworthiness

Vol. 23, Iss. 31, AUGUST 2017

PDF  Article PDF Version

Received: 14 July 2017

Received in revised form: 28 July 2017

Accepted: 11 August 2017

Available online: 29 August 2017

Subject Heading: BUSINESS VALUE

JEL Classification: C44, C51, C52, L25, L26

Pages: 1878–1892

https://doi.org/10.24891/fc.23.31.1878

Arinichev I.V. Kuban State University, Krasnodar, Russian Federation
iarinichev@gmail.com

Saibel' N.Yu. Kuban State University, Krasnodar, Russian Federation
saybel-natali@yandex.ru

Importance The article addresses the need to develop an integrated approach to creditworthiness assessment based on performance indicators of small businesses that characterize all aspects of their operations.
Objectives The purpose of the study is to develop an algorithm to evaluate the borrowing power and to build a logical model of creditworthiness of small businesses that would consider every facet of their operations and a low degree of financial stability.
Methods Using the methods of comparison and analogy, we review existing approaches to evaluation of small business creditworthiness.
Results The developed methodology rests on machine learning techniques, which make it sufficiently complete and detailed. The techniques imply finding, revealing and analyzing indicators that comprehensively describe the operations of small business entities. We propose a systematization of factors of creditworthiness, which will enable a straightforward and efficient assessment.
Conclusions The existing methods to assess small business creditworthiness mainly focus on business analysis. However, to provide an adequate assessment, the methodology should take into account not only financial performance, but also business reputation, efficiency of basic resource utilization, level of competitiveness, and market position.

Keywords: creditworthiness, small business, scoring, risk, machine learning

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