HIT SENTINEL An Adaptive Multi-Model Early Warning Framework for Predicting Corporate Failure in Emerging Markets

High-profile corporate collapses-Steinhoff International, Tongaat Hulett, VBS Mutual Bank, and Enron Corporation-expose a persistent gap in existing forensic models: no single framework provides timely, contextually appropriate early warnings across different manipulation typologies in emerging markets. This paper introduces HIT Sentinel, an adaptive ensemble-based early warning system designed specifically for Sub-Saharan African capital markets. Using a longitudinal panel dataset of 194 firm-year observations drawn from 15 listed companies on the Johannesburg Stock Exchange (JSE) and Zimbabwe Stock Exchange (ZSE) spanning 1993-2025, the system integrates seven established forensic benchmark models with 22 efficiency ratios and multiple machine learning classifiers-Random Forest, Isolation Forest, Gaussian Mixture Models, and XGBoost. A tiered training strategy and nested time-series cross-validation with Bayesian hyper-parameter optimisation eliminate look-ahead bias and address the inherent class imbalance (16.5% fraud prevalence). The final Random Forest ensemble achieved an AUC-ROC of 0.891 and AUC-PR of 0.723 under realistic rolling-window validation, with precision of 76.9%, recall of 83.3%, and F1-score of 80.0%. Notably, the model correctly assigned low fraud probability (12.5%) to Steinhoff-honestly reflecting that vendor finance manipulation leaves a distinctive financial footprint not captured by current features-while accurately flagging Tongaat (65.9%), VBS (87.2%), and Enron (73.4%). Applied to OK Zimbabwe Limited, which entered voluntary corporate rescue in February 2026, the system produced a fraud probability of 3.0%, correctly distinguishing severe operational distress from fraudulent manipulation and directing rescue practitioners toward liquidity and leverage interventions. An interactive Dash/Plotly dashboard renders outputs across three risk layers, making findings accessible to non-technical stakeholders. This research contributes a replicable, interpretable, and scalable early warning blueprint for capital market regulators, investors, auditors, and corporate rescue practitioners across Sub-Saharan Africa.