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All Journal International Journal of Digital Entrepreneurship and Business (IDEB)
J Trujillo T, Pedro
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Economics, Econometrics & Finance Education Environmental Science Social Sciences Other

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Predicting Financial Distress in a Turbulent World: a Comparative Machine Learning Analysis Across Nations Syahril; J Trujillo T, Pedro
International Journal of Digital Entrepreneurship and Business Vol 6 No 2 (2025): International Journal of Digital Entrepreneurship and Business (IDEB)
Publisher : Universitas Jakarta Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52238/ideb.v6i2.292

Abstract

This study evaluates the performance of six machine learning models in predicting financial distress, focusing on Indonesia and comparing with other nations. Using metrics like accuracy, AUC Macro, F1 Macro, F1 Weighted, and Log Loss, we find the Random Forest model with a Standard Scaler Wrapper performs best across most metrics, while LightGBM with MaxAbs Scaler is preferred for deployment due to its robustness and scalability. We analyze feature importance of identifying key factors influencing financial distress, such as investment growth, GDP growth, and economic uncertainty. Our findings highlight the critical role of machine learning in economic forecasting and policymaking, emphasizing the importance of digital optimization and AI-driven decision-making in addressing global financial stability.
Co-Authors Syahril