Cooperative credit risk prioritization for small and medium-sized enterprises relies on a transparent multi-criteria model implemented through an Excel-reproducible framework, according to a study published in Neutrosophic Sets and Systems.
- A pilot AHP-TOPSIS multi-criteria model handles incomplete and dispersed information for seven active SME loans.
- AHP pairwise comparisons established validated criterion weights with a consistency ratio of 0.0440, remaining well below the 0.10 threshold.
- The framework operates without proprietary software, offering resource-constrained savings and credit cooperatives an accessible evaluation method.
Credit risk assessment within savings and credit cooperatives often encounters obstacles due to dispersed and heterogeneous data. Ramiro Javier Castillo Herrera and Dayron Rumbaut Rangel detailed these challenges in their research paper titled Multi-Criteria Credit Risk Prioritization in Cooperative SMEs Using AHP-TOPSIS: A Reproducible Pilot with Pseudonymized Data, published in volume 101 of the journal on October 16, 2026.
The pilot study audited a pseudonymized dataset comprising seven active SME loans to test relative risk prioritization. By utilizing Analytic Hierarchy Process (AHP) pairwise comparisons alongside Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the authors structured qualitative and quantitative risk factors into a unified matrix.
| Parameter | Pilot Measurement | Target Standard |
|---|---|---|
| Sample Size (n) | 7 SMEs | Active Loan Portfolio |
| Consistency Ratio (CR) | 0.0440 | < 0.10 |
| Software Dependency | Excel-Reproducible | Non-Proprietary |
The methodology addresses resource limitations typical of smaller cooperative financial institutions. Rather than depending on expensive, closed-source risk management platforms, the pilot demonstrates that standard spreadsheet infrastructure can maintain analytical rigor.
The study highlights traceability and interpretability as core advantages for cooperative boards reviewing credit exposures. Since tracking downloads began, the paper has drawn interest from analysts examining quantitative risk methods in alternative lending sectors.
Disclaimer: The information provided in this article is for educational and informational purposes only and does not constitute financial advice.
