265. Evaluating the Robustness and Practical Utility of XAI-Driven Early Warning Systems in firm distress prediction
Invited abstract in session TC-67: Business Management in Dynamic Emerging Markets, stream Selected Aspects of International Finance and OR.
Tuesday, 10:30-12:00Room: KOL – PC Raum 3
Authors (first author is the speaker)
| 1. | Trustlord Marecha
|
| School of Commerce, University of Johannesburg | |
| 2. | Helper Zhou
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| 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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