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Assessing regional competitiveness in Peru: An approach using nonlinear machine learning models.

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Microfinance institutions failure prediction in emerging countries, a machine learning approach.

Yvan J Garcia-Lopez1,2, Patricia Henostroza Marquez1,2, Nicolas Nuñez Morales1,2

  • 1CENTRUM Católica Graduate Business School (CCGBS), Lima, Peru.

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A new Adjusted Gross Granular Model (ARGM) predicts microfinance institution failures with high accuracy. This machine learning tool aids financial regulators in emerging markets, preventing economic instability and protecting clients.

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Area of Science:

  • Financial Risk Management
  • Computational Finance
  • Economic Inclusion

Background:

  • Microfinance institutions (MFIs) are crucial for economic inclusion, but their failure poses significant risks.
  • Predicting MFI failures is challenging due to imbalanced and complex financial data.
  • Existing models often lack practical applicability in real-world regulatory environments.

Purpose of the Study:

  • To develop a practical and accurate model for predicting microfinance institution failures.
  • To enhance financial stability in emerging markets through early risk detection.
  • To provide regulators with a reliable tool for proactive intervention.

Main Methods:

  • Utilized granular computing and machine learning techniques.
  • Developed the Adjusted Gross Granular Model (ARGM).
  • Analyzed data from 56 Peruvian financial institutions (2014-2023).

Main Results:

  • The ARGM achieved nearly 90% accuracy in predicting failures.
  • The model demonstrated over 95% accuracy in identifying stable institutions.
  • Six institutions (20%) were flagged as high-risk, demonstrating practical application.

Conclusions:

  • The ARGM is a highly accurate and practical tool for predicting MFI failures.
  • This model can significantly aid financial regulators in emerging markets to prevent crises.
  • Proactive intervention based on ARGM predictions can safeguard economic inclusion and community savings.