Effects of Civil Wars on the Financial Soundness of Banks: Evidence from Sudan Using Altman’s Models and Stress Testing
Bibliographic record
Abstract
This study assesses the financial soundness of Sudanese commercial banks during escalating civil conflict by integrating Altman’s Z-score models with scenario-based stress testing. Using audited financial data from 2016 to 2022 (pre-war) and projections through to 2028, the analysis evaluates resilience under low- and high-intensity conflict scenarios. Altman’s Model 3 (for non-industrial firms) and Model 4 (for emerging markets) are applied to capture liquidity, retained earnings, profitability, and leverage dynamics. The findings reveal relative stability between 2017–2020 and in 2022, contrasted by significant vulnerability in 2016 and 2021 due to macroeconomic deterioration, sanctions, and political instability. Liquidity emerged as the most critical driver of Z-score performance, followed by earnings retention and profitability, while leverage showed a context-specific positive effect under Sudan’s Islamic finance framework. Stress testing indicates that even under low-intensity conflict, rising liquidity risk, capital erosion, and credit risk threaten sectoral stability by 2025. High-intensity conflict projections suggest systemic collapse by 2028, characterized by unsustainable liquidity depletion, near-zero capital adequacy, and widespread defaults. The results demonstrate a direct relationship between conflict duration and systemic fragility, affirming the predictive value of Altman’s models when combined with stress testing. Policy implications include the urgent need for enhanced risk-based supervision, Basel II/III implementation, crisis reserves, contingency planning, and coordinated regulatory interventions to safeguard the stability of the banking sector in fragile states.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".