Application of Econometric Techniques and Machine Learning Algorithms for Credit Risk Assessment of Small and Medium-sized Enterprises (SMEs)

Book chapter

Peer reviewed

Bogdan Pleshkevich and Liang Han

Handbook of Financial Econometrics, Statistics, Technology and Risk Management

World Scientific Publishing Company

05/2025

Abstract

Credit risk modeling • SME • Logistic regression • Machine learning • Coarse classification

The assessment of corporate credit risk is definitive for daily operations of financial institutions.

While small and medium-sized firms largely contribute to the economic growth

of global economies, quantitative assessment faces a number of challenges. This chapter

aims to summarize the key developments in credit risk modeling of small and medium-sized

enterprise (SME), taking into account modern estimation techniques. Through a literature

review and an empirical study, it outlines the historical evolution of credit risk evaluation

of SMEs and presents current trends in the field. By formulating key limitations, this chapter

focuses on revealing recent contributions that utilized advanced techniques for SME

scoring, such as higher performance and robustness.

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Title: Application of Econometric Techniques and Machine Learning Algorithms for Credit Risk Assessment of Small and Medium-sized Enterprises (SMEs)
Creators: Bogdan Pleshkevich (Author) - Henley Business School, University of Reading, UK
Liang Han (Corresponding Author) - University of Surrey, SII DUFE (MS04)
Publication Details: Handbook of Financial Econometrics, Statistics, Technology and Risk Management
Publisher: World Scientific Publishing Company
Publication Date: 05/2025
Date accepted for publication: 04/05/2024
Identifiers: 99971366002346
Academic Unit: SII DUFE (MS04)
Language: English
Resource Type: Book chapter