265. Evaluating the Robustness and Practical Utility of XAI-Driven Early Warning Systems in firm distress prediction
Invited abstract in session Business Management in Dynamic Emerging Markets, stream Selected Aspects of International Finance and OR.
Authors (first author is the speaker)
| 1. | Trustlord Marecha
|
| School of Business Management, University of Johannesburg | |
| 2. | Helper Zhou
|
| School of Accounting, Economics and Finance, University of KwaZulu Natal |
Abstract
South African SMMEs face >70% failure rates in the first five years due to opaque risk signals. This study evaluates the real-world applicability and robustness of an XAI-driven Early Warning System (EWS) for firm distress prediction, moving beyond predictive accuracy to assess actionability, temporal resilience, and performance under data scarcity. Using 28,400 SMMEs (2016–2024), SHAP/LIME-augmented Gradient Boosting/LightGBM models provide actionable insights. The XAI-EWS emerges as a deployable tool for lenders and agencies.
Keywords
- Artificial Intelligence
- Finance
- Knowledge, Technology, and Innovation Management
Status: accepted
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