Machine Learning Analysis of Financial Risk Dynamics in Micro-, Small, and Medium Enterprises

Dražen Božović, Nataša Perović (), Marinko Aleksić, Ivana Rašović and Oto Iker
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Dražen Božović: Faculty of Business Economics and Law, University “Adriatic”, Rista Lekića 16, 85000 Bar, Montenegro
Nataša Perović: Faculty of Business Economics and Law, University “Adriatic”, Rista Lekića 16, 85000 Bar, Montenegro
Marinko Aleksić: Faculty of Maritime Studies and Tourism, University “Adriatic”, 85000 Bar, Montenegro
Ivana Rašović: Faculty of Business Economics and Law, University “Adriatic”, Rista Lekića 16, 85000 Bar, Montenegro
Oto Iker: Faculty of Maritime Studies and Tourism, University “Adriatic”, 85000 Bar, Montenegro

Risks, 2025, vol. 13, issue 12, 1-34

Abstract: This study examines the use of artificial neural networks (ANNs) to classify financial risks in micro-, small-, and medium-sized enterprises (MSMEs) in Montenegro and the wider Western Balkan region. The economies in this region share structural similarities, such as a high concentration of MSMEs, limited access to finance, and vulnerability to macroeconomic volatility, which make financial risk assessment particularly challenging. Traditional statistical and econometric methods often fail to capture the complex, nonlinear interdependencies among financial and operational indicators, resulting in the inaccurate classification of high-risk MSMEs. By applying advanced machine learning (ML) techniques, neural networks (NNs) can identify intricate patterns in multidimensional financial data, significantly improving the accuracy and reliability of risk classification. In this research, a predictive model was developed using key financial and operational variables of MSMEs, enabling the accurate classification of MSMEs in terms of financial instability and insolvency. Empirical validation shows that NNs outperform conventional methods in accuracy, sensitivity, and generalisation. This approach offers tangible benefits for investors, credit institutions, and MSME managers, supporting improvements in early warning systems, optimisation of credit decision-making, and strengthening MSMEs’ financial resilience and sustainability. The methodology also advances risk quantification tools, providing robust indicators for strategic planning and resource management. By focusing the analysis on Montenegro and the Western Balkans, this study demonstrates that regional economic and structural similarities support the adaptation of NN models for precise financial risk classification, offering actionable insights to enhance MSME performance and regional economic stability.

Keywords: financial risk pattern; classification; neural networks; micro, small- and medium-sized enterprises (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:13:y:2025:i:12:p:240-:d:1811764

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