Effects of Civil Wars on the Financial Soundness of Banks: Evidence from Sudan Using Altman’s Models and Stress Testing

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.

1. Introduction

The outbreak of civil war in Sudan in April 2023 raised serious concerns about the solvency and operational resilience of the country’s commercial banking system. In conflict-affected economies, banking instability can amplify economic crises, triggering systemic financial collapses and long-term developmental setbacks. As commercial banks serve as key conduits for financial intermediation, credit allocation, and economic growth, assessing their financial soundness during periods of armed conflict becomes a critical priority for economic stakeholders and policymakers.

Forecasting tools—particularly those focused on bankruptcy risk and stress resilience—are indispensable in such contexts as they enable timely assessments of institutional vulnerabilities and inform strategic decisions to mitigate the socioeconomic fallout of financial disruptions.

Despite the ongoing hostilities in Sudan, ensuring the continued functionality of the banking sector—especially its capacity to facilitate humanitarian aid, preserve public trust, and stabilize domestic markets—remains a strategic imperative. Maintaining operational liquidity and resilience in Sudanese banks is crucial for supporting both economic stability and humanitarian efforts during conflict (Mustafa, 2019; Mustafa, 2020).

The literature offers substantial evidence regarding the macroeconomic costs associated with civil wars. Novta and Pugacheva (2020) found that a decade of armed conflict typically results in a 28% decline in GDP per capita, a 25% contraction in private consumption, and substantial reductions in trade volumes; namely, 58% for exports and 34% for imports. Similar patterns have been observed globally: Bosnia and Herzegovina’s GDP fell to just 20% of pre-war levels by 1995; Kosovo’s conflict inflicted losses exceeding EUR 30 billion; Libya experienced a 24% GDP decline in 2014; and Yemen and Syria both saw GDP contractions of over 25% in the early stages of war (IMF, 2017).

In addition to economic stagnation and inflation, conflicts disrupt institutional and financial stability, with capital flight, declining foreign investment, weakened private sector confidence, and deteriorating trade balances being common outcomes (Weinstein & Imai, 2000; Issa et al., 2022). Recent studies have further emphasized that financial instability is heightened by inflationary pressures and the relative youth of banks, while stronger capitalization and larger bank size tend to improve resilience (Novta & Pugacheva, 2020). In a related analysis, Mustafa (2023) applied Basel II-based stress testing to Sudanese banks and found that civil conflict had severely eroded the banking sector’s resilience, emphasizing the need for enhanced regulatory intervention by the Central Bank of Sudan.

Although the economic costs of civil conflicts have been widely discussed, active civil conflicts and their implications for banking sector stability have received scant attention. This study seeks to fill this gap by assessing the effects of the Sudanese civil conflict on the soundness of its commercial banking sector. Utilizing a dual methodological approach—namely, Altman’s Z-score bankruptcy prediction models and stress tests—this study examines the performance of commercial banks in Sudan from 2016 to 2022 (pre-war) and forecasts their financial indicators until 2028. In doing so, this research aims to assess the resilience of the Sudanese banking system to current and future shocks associated with the ongoing conflict in Sudan, providing valuable insights for financial regulators, policymakers, and international development partners concerned with the resilience of the banking sector in conflict-affected economies.

2. Literature Review

Civil wars represent one of the most profound disruptions to a country’s financial and institutional stability. Their effects extend beyond physical destruction to include deterioration in investor confidence, collapse in governance, and severe strain on banking systems. In fragile economies such as Sudan, the banking sector is particularly vulnerable due to weak capital buffers, underdeveloped regulatory oversight, and exposure to politically connected lending. In this section, the empirical and theoretical literature on how civil conflicts impact banking systems is critically reviewed and the applicability of Altman’s Z-score models and stress testing frameworks for assessing financial resilience is explored.

Recent empirical work (e.g., Rother et al., 2016) has highlighted how conflicts impair core banking functions, such as credit intermediation, deposit mobilization, and payment processing, resulting in systemic financial fragility. Although Rother et al. focused on conflict zones outside Sudan, their findings are relevant due to similar structural characteristics: institutional fragility, macroeconomic volatility, and shallow capital markets. Sudan’s ongoing civil war—marked by prolonged violence and monetary instability—mirrors many of the stress channels outlined in that study, including capital flight and loss of depositor confidence.

Altman’s Z-score models have been widely used to detect early signs of financial distress, particularly in firms and institutions operating in high-risk environments. The theoretical basis of the model lies in combining weighted financial ratios (liquidity, retained earnings, profitability, leverage) into a composite index to predict the risk of bankruptcy. This approach is especially relevant for Sudan’s banking system, for which publicly available data are limited and standard market-based risk metrics are unreliable. The Altman’s Z-score models, Model 3 is designed for non-manufacturing firms such as banks, while Model 4 is tailored for emerging markets, making both suitable for Sudan’s unique context of an Islamic, non-industrialized, and conflict-affected economy. Cındık and Armutlulu (2021) and Boanta et al. (2020) have confirmed the validity of these models in distressed and transition economies.

Several empirical studies have demonstrated the effectiveness of Altman’s Z-score models in evaluating financial soundness in banking sectors operating under conditions of instability, fragility, or post-conflict recovery. For instance, Elia et al. (2021) applied the Altman Z″-score—an adaptation of the original model designed for emerging markets—to assess the financial health of Alpha banks in Lebanon during 2009–2018. Their findings revealed significant levels of distress, underscoring the model’s utility in fragile, post-civil war environments.

Similarly, Nath et al. (2020) employed the original Z-score model to evaluate state-owned commercial banks in Bangladesh—a country that frequently faces economic and governance challenges—and found most banks to be at high risk of financial distress. In the context of another emerging economy, Shrestha et al. (2025) illustrate the application of Altman Z-score models in fragile banking systems. Analyzing Nepalese private banks, they found capital adequacy to be the most significant determinant of financial distress, while liquidity, profitability, and credit risk were less influential. Their findings demonstrate how Altman’s methodology can serve as an effective tool to identify early warning signals and guide regulators and policymakers in maintaining financial stability in fragile states.

Moreover, the work of Altman et al. (2017) offered further theoretical justification, affirming that the Z″-score model is particularly suited for non-U.S. and emerging market environments. Therefore, the application of this model to the Sudanese banking sector during the ongoing civil conflict is methodologically sound and supported by international precedents.

Case studies from Ukraine, Libya, and Syria have further illustrated the fragility of banking systems under conflict. For example, the Russian–Ukrainian conflict has severely weakened domestic banking sectors and exposed global banks to heightened credit, operational, and market risks through both direct and indirect channels (OECD, 2022). Similarly, drawing on empirical analyses, Sahyouni and Wang (2019) and Diwani (2022) provide evidence that the Syrian civil war severely disrupted the banking sector, triggering a substantial escalation in non-performing loans and a marked contraction in banking assets. Their findings highlight wartime declines in liquidity creation, deterioration in overall bank performance, and a pronounced erosion of sectoral competitiveness.

Armed conflicts exert multidimensional stress on financial systems, undermining institutional coherence, disrupting intermediation, and eroding public trust in formal financial infrastructure. A growing body of empirical research across conflict-affected states—such as Ukraine, Sudan, Afghanistan, Syria, and Yemen—reveals recurrent financial vulnerabilities alongside locally specific institutional breakdowns.

In Ukraine, Arzhevitin et al. (2023) document that, in response to martial law, the Ukrainian banking sector swiftly adapted by increasing reliance on short-term deposits and heightened exposure to currency and market risks—strategies aimed at mitigating liquidity and credit risks under conditions of severe uncertainty.

Sudan experienced a near-total collapse of its banking system during the 2023 conflict, with over 70% of branches closing and a systemic liquidity freeze rendering formal channels inoperable for most citizens (ACAPS, 2023; Mustafa, 2023). The emergence of parallel banking systems aligned with opposing political authorities further fragmented financial governance and deepened macroeconomic instability.

In contexts such as Yemen and Afghanistan, prolonged conflict and institutional disintegration have substantially undermined formal banking systems, resulting in asset deterioration and the expansion of informal financial mechanisms (Huddleston & Wood, 2021; Rupin, 2000). While banks adopt adaptive strategies to address immediate operational and liquidity risks, these measures contribute to further fragmentation of financial governance and heighten broader macroeconomic instability.

In Syria, Alyousef (2022) demonstrates that the Syrian conflict significantly undermined private banking performance, with conflict metrics such as refugee outflows and exchange rate shocks driving down profitability. Complementing this, Alyousef (2022) provides a broader economic analysis, revealing how prolonged conflict eroded domestic capital and disrupted investment dynamics—fundamental contributors to the deterioration of banking systems.

A persistent theme across these cases is the fragmentation of monetary authority and regulatory institutions. While direct studies on dual central bank regimes in Yemen and Sudan lack DOIs, conflict-driven institutional fragmentation more broadly significantly raises systemic banking crisis risk (Compaoré et al., 2020). Conflict-driven financial disruptions are not confined to domestic boundaries. Such contagion dynamics were previously observed during the Arab Spring, reinforcing the interconnectedness of financial systems in the face of geopolitical instability (Mousavi & Ouenniche, 2014; Malik & Awadallah, 2013).

Post-conflict financial recovery depends critically on the speed, transparency, and legitimacy of institutional reforms. Lebanon’s recent attempt to enact banking sector restructuring (2025) underscores the necessity of legal clarity and governance renewal in re-establishing depositor confidence and securing multilateral assistance. IMF analyses (IMF, 2022) have emphasized that durable recovery requires the recapitalization of banks, rehabilitation of central banking functions, and restoration of critical infrastructure.

Cross-country statistical evidence confirms that civil conflicts significantly elevate financial fragility. According to IMF (2020) and Compaoré et al. (2020), the probability of a systemic banking crisis increases by a factor of 2.5 in conflict-affected developing economies. This underscores the imperative of proactive regulatory safeguards, stress testing frameworks, and contingency planning tailored to the context of fragile states.

Despite the global attention to banking sector resilience during conflict, relatively few empirical studies have focused specifically on Sudan. Empirical analyses indicate that civil conflicts in Sudan have profoundly disrupted the banking sector, resulting in fragility, impaired monetary governance, and diminished financial resilience. The studies IMF (2022, 2024), highlight how prolonged political instability and conflict exacerbate macroeconomic volatility, impede effective regulation, and increase the likelihood of non-performing loans and liquidity shortages.

Stress testing analyses reveal that Sudanese banks’ reliance on equity-based and Sharia-compliant financing models partially mitigates risk, yet structural vulnerabilities remain, particularly in older and undercapitalized institutions (Mustafa, 2023).

Furthermore, IMF reports underscore the spillover effects of Sudan’s instability on regional financial systems, emphasizing the necessity of rapid institutional reform, enhanced regulatory oversight, and targeted recapitalization measures to restore sectoral stability (IMF, 2022, 2024). Collectively, these findings illustrate that conflict-driven financial disruptions in Sudan require both domestic and international policy interventions to safeguard banking system resilience.

Mennawi (2020) further connected Sudan’s macroeconomic instability to declining bank performance, highlighting the complex interplay between systemic risk and financial sector fragility. Although high leverage is generally linked with elevated financial risk, in the context of Sudanese Islamic banking—where interest-bearing instruments are prohibited—this leverage may instead reflect reliance on equity-based financing models such as Mudarabah and Musharakah. These models, rooted in profit and loss sharing, are central to Islamic finance and are utilized by banks in Sudan to align with Sharia principles (Beck et al., 2020). In this context, capital appears to play a dual role: while it exhibits a mild negative correlation with the risk of liquidity, it also contributes to banking stability by enhancing market confidence and facilitating access to emergency liquidity.

Corporate governance is another important dimension influencing banking stability in conflict-affected Islamic banking systems. Evidence from Issa et al. (2022) shows that robust governance frameworks in Islamic banks can promote sustainable practices such as green banking, enhance market confidence, and contribute to long-term resilience. Similarly, Gazi et al. (2024) identify bank-specific and macroeconomic determinants—such as capital adequacy, liquidity management, and GDP growth—that directly influence profitability in Shariah-compliant banking institutions. These insights are particularly relevant to Sudan, where the banking system operates entirely under Islamic principles and must simultaneously address conflict-driven volatility.

This study aims to fill the gap in the existing literature by applying Altman’s models and stress testing techniques to Sudanese data from 2016 to 2022 and forecasting up to 2028 under varying conflict scenarios.

This research is guided by the following questions:

• How accurately do Altman’s Z-score models predict the financial stability of Sudanese commercial banks, using audited financial ratios from 2016 to 2022?
• Which model (Model 3 or Model 4) demonstrates stronger predictive validity under Sudan’s conditions?
• How do conflict-driven stress scenarios affect liquidity, capital adequacy, and credit risk among Sudanese commercial banks?

Through integrating these models with scenario-based stress testing, this study contributes a novel framework to assess financial sector resilience in conflict-affected emerging economies.

The following sections are organized as follows: Section 3 and Section 4 details the research design and data acquisition, including an explanation of the utilized data sources. The findings and their interpretation are presented in Section 5, followed by our conclusions and practical recommendations in Section 6.

2.1. Altman’s Z-Score Models and Bankruptcy Prediction

Altman’s Z-score models have long been established as robust predictors of corporate insolvency, offering empirical metrics to evaluate the financial health of firms. In this study, two specific formulations are utilized: Model 3, which is tailored for non-manufacturing enterprises, and Model 4, which is designed for emerging market contexts. These models use weighted financial ratios to compute a composite score that categorizes firms into “Safe,” “Grey,” or “Distress” zones, allowing for early identification of bankruptcy risk.

The Altman Z′-score and Z″-score analyses of Sudanese commercial banks from 2016 to 2022 provide insights into the financial soundness of banks leading up to the civil unrest in Sudan. Rashid et al. (2023) have provided an extensive review of the Altman Z-score models, exploring their evolution and applications across various industries. A detailed examination of the models used in this study is provided in the following:

Model 3 (Z′) is designed for non-manufacturing firms, and includes four key financial ratios (liquidity, retained earnings, profitability, and leverage) without a constant term. It is calculated, according to Bondar and Mudzakar (2023), as follows:

Z′ = 6.56X₁ + 3.26X₂ + 6.72X₃ + 1.05X₄

(1)

where:

• X₁ (liquidity): This ratio indicates a bank’s liquidity, efficiency, and overall financial soundness through comparison with its total assets (including both short- and long-term assets). The liquidity ratio reflects a bank’s liquidity and its capacity to pay short-term debts to creditors.
• X₂ (retained earnings): This ratio reflects a bank’s cumulative profitability, assessing profits that have been held for many years from net earnings and not distributed to shareholders.
• X₃ (profitability): This ratio measures a bank’s operational efficiency and profitability, independent of its financing and tax structures.
• A higher X₃ value reflects better asset productivity, implying that the bank is generating more earnings from its asset base, which is a positive sign for creditors and investors, according to Rashid et al. (2023).
• X₄ (leverage): This ratio measures a bank’s financial leverage and solvency by comparing its equity to its liabilities. A high X₄ value suggests strong equity relative to liabilities, indicating that the bank has a solid buffer to absorb potential losses, thereby lowering its risk of bankruptcy. A robust X₄ ratio demonstrates that the bank has sufficient equity to cover its liabilities, enhancing its creditworthiness and financial stability according to Rashid et al. (2023). Overall, higher values generally indicate a healthier financial position and a lower risk of bankruptcy.

The Z′-score categorizes companies as follows: safe zone (Z′ > 2.6), grey zone (1.1 < Z′ ≤ 2.6), and distress zone (Z′ ≤ 1.1).

Model 4 (Z″) is tailored for emerging markets and includes the same ratios as the Z′-score but integrates a constant term for better predictive accuracy in volatile environments. It is calculated as follows:

Z″ = 3.25 + 6.56X₁ + 3.26X₂ + 6.72X₃ + 1.05X₄

(2)

where X₁, X₂, X₃, and X₄ are as defined for the Z’-score.

The Z″-score is used to categorize companies as follows: safe zone (Z″ > 5.85), grey zone (4.35 < Z″ ≤ 5.85), and distress zone (Z″ ≤ 4.35).

Cındık and Armutlulu (2021) have highlighted the effectiveness of these models in detecting early signs of financial distress in fragile economies, while Badea and Matei (2016) confirmed their adaptability to banking institutions given proper calibration.

In conflict-affected states, where external shocks are frequent and severe, the ability of Z-score models to offer early warnings is particularly valuable. These models have not been widely applied to the Sudanese banking sector under conditions of ongoing war, which this study aims to address through the use of audited financial statements to compute Z-scores and interpret systemic vulnerabilities in Sudanese commercial banks.

2.2. Stress Testing in Conflict-Prone Economies

Stress testing constitutes a forward-looking analytical framework designed to assess the resilience of financial institutions under extreme (but plausible) adverse scenarios. As a core component of macroprudential surveillance, it plays a critical role in identifying systemic vulnerabilities before they materialize. Ong and Jobst (2020) emphasized the importance of stress testing in financial stability monitoring, particularly within jurisdictions exposed to geopolitical instability, macroeconomic volatility, or fiscal shocks.

Stress testing is particularly relevant in conflict-affected economies, as it provides a mechanism to quantify the responsiveness of key financial soundness indicators—such as capital adequacy, asset quality, and liquidity—to escalating risk. Empirical evidence from fragile and war-torn states supports this approach. Studies focused on Ukraine (Arzhevitin et al., 2023), Syria (Alyousef, 2022), and Libya (IMF, 2017) have demonstrated that prolonged armed conflicts typically undermine the stability of banking systems through a combination of capital erosion, increased non-performing loans (NPLs), and liquidity shortfalls.

Building on these findings, Compaoré et al. (2020) provided empirical support for a positive correlation between conflict duration and the probability of systemic banking crises. Their cross-country study emphasized that longer conflict periods significantly elevate the banking sector’s fragility, underscoring the need for multi-scenario stress testing frameworks that incorporate both the intensity and duration of conflict.

The IMF (2020) further substantiated this relationship through a large-scale empirical analysis of 92 developing countries spanning 1970 to 2016. Their findings indicated that the probability of a banking crisis rises from 16.4% for conflicts lasting two years to 25% for conflicts persisting over a decade. These benchmark figures served as the basis for constructing conflict-adjusted stress scenarios in the present study.

Despite the widespread application of stress testing in global financial oversight research, its utilization in Sudan—particularly in the context of the ongoing civil war—remains limited. This study addresses this methodological gap by integrating Altman’s Z-score diagnostics with tailored stress testing scenarios calibrated to Sudan’s conflict-specific conditions. The analytical framework draws upon the studies of the IMF (2020) and Compaoré et al. (2020) to formulate two hypothetical conflict scenarios:

Low-conflict scenario (16.4%): It is assumed that the current conflict extends through 2025, followed by macroeconomic stabilization and partial recovery beginning in 2026.

High-conflict scenario (25%): It is projected that the conflict persists through to 2028, with political and economic stabilization only occurring in 2029.

Under both scenarios, analysis was performed to evaluate the projected performance of Sudanese commercial banks in terms of three core financial dimensions: liquidity risk, capital adequacy, and credit risk. The objective was to quantify the extent of financial deterioration associated with each scenario and to assess institutional soundness under conditions of sustained instability.

Importantly, the design of these stress tests incorporates structural factors that are specific to Sudan, including macroeconomic fragility, political volatility, and data limitations. This contextual adaptation ensures that the model remains relevant, realistic, and capable of informing both academic inquiry and policy intervention in fragile state environments.

2.3. Literature Gaps and Contribution of the Study

Although extensive research has focused on the effects of civil wars on macroeconomic performance (e.g., GDP, trade, inflation), far fewer studies have examined how such conflicts affect financial stability in fragile states. Moreover, very few studies have combined predictive models (e.g., Z-scores) with stress testing simulations to provide a comprehensive framework for evaluating bank financial resilience in war-torn economies.

This study contributes to addressing this gap by focusing on Sudanese commercial banks, applying Altman’s bankruptcy prediction models alongside stress testing simulations. Given the central role of banks in economic recovery and stability, evaluating their robustness under crisis conditions is essential for designing responsive regulatory and financial interventions.

Three mains contributions emerge from this study: (1) It is one of the first to apply both of Altman’s Model 3 and Model 4 to Sudanese banks using historical and projected financial data. (2) Stress testing scenarios explicitly based on Sudan’s conflict context are developed, considering stress intensities of 16.4% and 25%. (3) Z-score analysis is integrated with stress testing to evaluate bank-level stability over time, offering a more dynamic and robust financial risk management framework than either method alone.

3. Research Methodology

3.1. Justification and Model Selection

This study adopts a dual-method approach that integrates Altman’s Z-score models with conflict-adjusted macroeconomic stress testing to evaluate the financial soundness of Sudanese commercial banks. The rationale for selecting Altman’s Model 3 (for non-manufacturing firms) and Model 4 (for emerging markets) lies in their established predictive capacity in contexts similar to Sudan’s economic and institutional environment. Sudan’s banking sector—which is largely composed of Islamic financial institutions—operates in a fragile, conflict-prone economy where traditional market-based risk indicators are often unavailable or unreliable due to political instability, macroeconomic shocks, and underdeveloped financial markets.

Altman’s models, which rely solely on firm-level financial statement data, are particularly well-suited for such environments. These data are accessible through official channels, including the Central Bank of Sudan and the e-library of the International Monetary Fund (IMF). These models allow for the computation of standardized insolvency risk scores using historical financial ratios, thereby enabling consistent longitudinal analysis.

The empirical validity of Altman’s Z-score models in fragile and underdeveloped financial systems has been documented in numerous studies. For instance, Altman et al. (2017) confirmed the effectiveness of Model 4 in predicting financial distress in emerging markets. Cındık and Armutlulu (2021) demonstrated the model’s applicability to banking institutions in crisis-affected economies. Similarly, Boanta et al. (2020), Elia et al. (2021), provided empirical evidence supporting the use of Z-scores in low-income, post-conflict, and high-volatility financial systems. In the context of Islamic and South Asian banking, Nath et al. (2020) have highlighted the robustness of the Z-score methodology in detecting early signs of financial instability.

Ashraf et al. (2019) compared traditional financial distress prediction models for firms at early and advanced stages of distress in Pakistan (2001–2015). They found that while the three-variable probit model exhibited the highest overall prediction accuracy, the Altman Z-score model was more effective in accurately predicting insolvency for both early- and advanced-stage distressed firms.

This baseline is essential for identifying the magnitude of deterioration under simulated conflict conditions using stress testing. Thus, in this study, the Z-score does not serve to directly assess the impact of war in Sudan but acts as a foundation for comparative analysis between normal (i.e., before conflict) and conflict-driven scenarios. This aligns with the broader objective of understanding the deterioration in financial soundness due to the civil war, using internationally recognized risk measurement tools adapted for use in data-constrained, conflict-affected environments.

3.2. Conceptual Link Between Variables and Altman Models

Altman’s Z-score methodology is based on a multivariate model using four financial ratios that act as early indicators of potential insolvency. According to Bondar and Mudzakar (2023), these include the following:

• Liquidity (X₁): It equals the ratio of working capital to total assets—measures a bank’s ability to meet its short-term obligations.
• Retained earnings (X₂): Expresses retained earnings as a share of total assets, reflecting the extent of self-financing and the historical buildup of profits.
• Profitability (X₃): A measure derived from EBIT over total assets, capturing the efficiency of asset use in producing operating earnings.
• Leverage (X₄): Calculated as equity divided by total liabilities, this indicator assesses financial stability and capital sufficiency.

Each of these ratios was calculated from audited financial statements of Sudanese commercial banks for the period 2016–2022. Their theoretical relationship to financial soundness is that declining liquidity, earnings, or capital adequacy typically signals a growing risk of insolvency, which the Z-scores aim to quantify.

3.3. Data Collection and Sample Description

Data sources: This study utilizes secondary financial data obtained from two main sources:

• Audited annual reports of Sudanese commercial banks, as published by the Central Bank of Sudan (2016–2022);
• Macroeconomic indicators and financial soundness measures from international databases and statistical bulletins.

The analysis focused on 27 Sudanese commercial banks that maintained continuous operations over the study horizon. Pre-war data from 2016–2022 served as the basis for forecasting outcomes under stress testing for the subsequent period, 2023–2028.

3.4. Stress Testing Design and Intensity Levels

3.4.1. Stress Testing Framework and Hypotheses

This study employs a forward-looking stress testing framework to evaluate the resilience of Sudanese commercial banks under varying levels of conflict-induced stress. The core objective was to assess how fluctuations in conflict intensity may impact key indicators of financial soundness—namely, capital adequacy, liquidity ratios, and credit risk—within a highly unstable socio-political environment. Stress testing scenarios were constructed to reflect Sudan’s unique economic and conflict conditions during the civil war period.

Two hypothetical conflict scenarios that correspond to different levels of stress intensity were developed for analysis, derived from empirical benchmarks established by the IMF (2020) and Compaoré et al. (2020). These scenarios are not predictive but, rather, serve as analytical constructs to test the sensitivity of Sudanese banks’ financial health under escalating political risk and prolonged conflict exposure.

3.4.2. Explanation of Stress Testing and Intensity Levels

Stress testing was performed to simulate how the banking sector may respond to ongoing conflict under two alternative trajectories. The stress intensity levels of 16.4% (low-conflict scenario level) and 25% (high-conflict scenario level) were derived based on historical economic contractions observed in post-conflict settings.

The stress scenarios were constructed depending on the duration of conflict as a baseline indicator to determine the appropriate intensity levels for hypothesis testing. This approach was guided by the IMF study (2020), which provided empirical evidence on the relationships between the duration of conflict and the probability of banking crises across 92 developing countries from 1970 to 2016. The IMF study indicated that the probability of a banking crisis increases with the length of conflict—from 16.4% for conflicts lasting two years to 25% for conflicts persisting over ten years. These two ratios (16.4% and 25%) were thus used as benchmark stress levels to model the potential impacts of varying conflict scenarios on the financial soundness of Sudanese commercial banks. Supporting evidence from Compaoré et al. (2020) reinforced these hypotheses, demonstrating that longer durations of armed conflict are statistically associated with a higher risk of systemic banking crises. Thus, the two-tiered stress scenarios used in this study were established based on empirical research, making them appropriate for assessing the resilience of Sudanese banks under conflict scenarios.

3.5. Financial Indicators and Measurement Tools

The stress testing framework employed several financial indicators, with their computation methods adopted from Pagratis et al. (2017). Liquidity risk was measured using the loan-to-deposit ratio, which gauges the bank’s ability to meet withdrawal demands while funding lending activities. Capital risk was assessed through the equity-to-deposits ratio, reflecting the adequacy of the bank’s capital base relative to deposit liabilities and overall solvency. Credit risk was captured by the ratio of non-performing loans (NPLs) to deposits, representing the exposure of deposit-funded assets to potential default.

Each ratio was calculated on an annual basis for both stress test scenarios (2022–2025 and 2022–2028), enabling the evaluation of risk trends and the banks’ financial resilience over time.

3.6. Integration of Altman Models and Stress Testing

Altman’s Z-score analysis facilitates a backward-looking diagnosis of pre-conflict bank soundness (2016–2022), while stress testing offers a forward-looking projection under hypothetical adverse conditions. The combination of these methods forms a comprehensive risk assessment framework: Altman’s models detect latent vulnerabilities, while stress tests simulate the systemic deterioration triggered by civil war. This integrated approach is particularly useful in conflict economies, where traditional econometric forecasting models may be infeasible due to data disruptions. Supporting evidence from Gerged et al. (2022), reinforced these hypotheses, demon-starting that, combining Altman’s Z-score with logistic regression enhances its predictive accuracy, confirming the model’s effectiveness when integrated with other statistical approaches. Moreover, this study underscores that stress testing yields the most meaningful insights when complemented by early-warning models, which together capture both immediate shocks and deeper structural vulnerabilities within banks. Importantly, the findings highlight the critical role of tailoring stress scenarios to the specific challenges faced by banks operating in volatile environments—such as political instability, currency depreciation, and liquidity shocks—conditions that closely mirror the Sudanese banking context. These insights suggest that a combined approach utilizing Altman’s Z-score, logistic models, and customized stress testing frameworks is particularly suitable for assessing bank resilience in conflict-affected economies.

4. Application of Altman’s Z-Score Models and Stress Testing

4.1. Macroeconomic Context of Sudan (2016–2022)

The period between 2016 and 2022 in Sudan was characterized by profound macroeconomic instability, political upheavals, and institutional uncertainty, all of which critically shaped the operational environment of the banking sector. These contextual conditions are essential for interpreting the application and outcomes of the Altman’s Z-score models, as they directly influenced the financial soundness indicators of Sudanese commercial banks.

From an international perspective, Sudan remained under comprehensive U.S. economic sanctions until late 2017, which severely restricted its access to the global financial system. Although the lifting of these sanctions was initially expected to facilitate economic reintegration and spur recovery, this anticipated stabilization was hindered by structural deficiencies, including limited foreign direct investment, a narrow export base, and systemic institutional weaknesses (IMF, 2017).

A major driver of financial sector volatility during this period was the persistent depreciation of the Sudanese Pound (SDG), which underwent multiple devaluations. These currency adjustments significantly impacted the real value of banks’ assets and liabilities, thus affecting their balance sheet integrity (Ashraf et al., 2019). Furthermore, Sudan experienced hyperinflationary pressures, with inflation rates surpassing 300% in some quarters, particularly during 2020–2021 (IMF, 2022). The resultant erosion of purchasing power and monetary instability introduced further challenges in terms of accurately assessing bank performance using traditional financial ratios. IMF (2022) demonstrates that high inflation has a significant negative effect on Sudanese banks’ financial stability, with inflation emerging as one of four key determinants of Altman Z-score stability metrics.

Politically, the 2019 revolution marked a pivotal moment in Sudan’s post-independence history. The overthrow of President Omar al-Bashir ushered in a transitional government that sought to implement economic reforms. However, the transitional period was marked by limited institutional capacity and fragile legitimacy, and its reform agenda was abruptly disrupted by a military coup in October 2021. This political regression undermined investor confidence, triggered capital flight, and exacerbated macroeconomic uncertainty (IMF, 2022).

Additionally, the COVID-19 pandemic (2020–2021) dealt a severe blow to Sudan’s already fragile economy, disrupting commercial activity and diminishing the operational performance of banks through declines in deposit mobilization, loan repayment capacity, and general financial intermediation (IMF, 2021).

Amid Sudan’s complex political and economic landscape, assessing the financial soundness of commercial banks within a contextual framework is crucial. This study applied Altman’s Z-Score Model 3 (for non-manufacturing firms) and Model 4 (for emerging markets) to examine the sector’s pre-conflict financial condition. The dual-model analysis enabled the identification of systemic vulnerabilities that were evident prior to the outbreak of civil war in 2023. This period constituted a critical extension of the underlying causes of the Sudanese conflict, as political unrest did not arise solely with the war but was preceded by multiple indicators that ultimately precipitated it.

4.2. Conceptual Framework for Altman Z-Score Analysis

The conceptual framework in this part of our study focuses primarily on the financial soundness of Sudanese commercial banks during the pre-civil war period in Sudan (2016–2022). Altman’s econometric models were used to predict their financial stability during this period, focusing on how specific financial ratios affect this stability. The four financial ratios in Altman’s econometric models (i.e., X₁ to X₄) represent the independent variables in this framework, which are posited to directly impact the financial soundness of Sudanese commercial banks (considered as the dependent variable). These ratios were derived from the Sudanese central bank’s audited financial statements for the years 2016–2022, making up the secondary dataset used in this study, and financial statements for the same period in the IMF e-library.

4.3. Stress Testing Scenarios and Hypotheses

Stress testing scenarios were constructed to reflect Sudan’s unique economic and conflict conditions during the civil war period.

Two hypothetical conflict scenarios that correspond to different levels of stress intensity were introduced for analysis. These scenarios are not predictive but serve as analytical constructs to test the sensitivity of Sudanese banks’ financial health under escalating political risk and prolonged conflict exposure.

4.3.1. Low-Conflict Scenario (16.4% Stress Level)

Under the first scenario, the financial stability of Sudanese commercial banks is tested under a moderate stress level of 16.4%. In this scenario, it is assumed that the current civil conflict continues through the end of 2025, followed by a return to stability in 2026. The recovery trajectory anticipates a gradual normalization of banking sector indicators to pre-conflict levels. During this period, moderate deterioration is expected in capital buffers and liquidity positions, reflecting the macroeconomic strains induced by extended but non-severe conflict.

4.3.2. High-Conflict Scenario (25% Stress Level)

The second scenario assumes an intensification of conflict, with stress levels reaching 25%, consistent with the upper-bound scenario modeled in the IMF’s multi-country analysis. This scenario envisions a sustained civil war through 2028, with political stabilization emerging only in 2029. Prolonged conflict is projected to severely impair the financial resilience of commercial banks, and the compound effects of declining liquidity, deteriorating asset quality, and weakening capital adequacy ratios may threaten the solvency and operational capacity of the sector.

The design of the stress testing framework is context-specific, integrating political, institutional, and macro-financial realities unique to Sudan. The dual-scenario structure facilitates a comparative analysis of the sector’s vulnerability under both moderate and extreme conflict conditions, offering insights into the threshold at which systemic financial risks may materialize.

5. Results and Discussion

5.1. Altman Z-Score Analysis (2016–2022)

5.1.1. Altman Z′-Score Analysis

Table 1. Model 3 (Z′-score) results for 2016–2022.

Model	Altman Z′-Score Analysis (2016–2022)
Year	X1 (Liquidity)	X2 (Retained Earnings)	X3 (Profitability)	X4 (Leverage)	Z’-Score Categories	Financial Zone
2016	0.26	0.012	0.023	0.125	2.05	Distress
2017	0.36	0.025	0.035	0.15	2.84	Safe
2018	0.53	0.03	0.047	0.175	4.12	Safe
2019	0.62	0.035	0.053	0.215	4.76	Safe
2020	0.67	0.045	0.042	0.175	5.01	Safe
2021	0.25	0.038	0.027	0.125	2.08	Distress
2022	0.64	0.023	0.018	0.075	4.47	Secure

Source: Authors’ calculations.

presents the Z′-scores (Model 3) for Sudanese commercial banks between 2016 and 2022. The findings indicate that the banks remained within the safe zone during most years (2017–2020 and 2022) with an average score of 4.25, suggesting strong financial health. However, in 2016 and 2021, the Z’-scores dropped below the distress threshold of 1.1, reflecting significant financial vulnerability during periods of heightened economic and political instability.

These fluctuations align with known macroeconomic disruptions in Sudan, indicating that the Z′-score model effectively captures financial stress linked to civil conflict and external shocks.

5.1.2. Altman Z″-Score Analysis

Similarly, Model 4 (

Table 2. Model 4 (Z″-score) results for 2016–2022.

Model	Altman Z″-Score Analysis (2016–2022)
Year	X1	X2	X3	X4	Z″-Score Categories	Financial Zone
2016	0.26	0.012	0.023	0.125	5.3	Grey
2017	0.36	0.025	0.035	0.15	6.09	Safe
2018	0.53	0.03	0.047	0.175	7.37	Safe
2019	0.62	0.035	0.053	0.215	8.01	Safe
2020	0.67	0.045	0.042	0.175	8.25	Safe
2021	0.25	0.038	0.027	0.125	5.33	Grey
2022	0.64	0.023	0.018	0.075	7.72	Secure

Source: Authors’ calculations.

)—which incorporates a different intercept and uses a threshold of Z″ < 4.35 for distress—presents an even stronger indication of stability for Sudanese banks. From 2017 to 2020 and again in 2022, all scores were well above the cutoff, averaging 7.49 and demonstrating consistent financial resilience. The only exceptions were 2016 and 2021, where the scores entered the grey zone, signaling moderate risk.

Model 4 reinforces the earlier findings and suggests that while banks recovered after 2016, systemic stress re-emerged in 2021—likely tied to renewed political turmoil.

5.1.3. Relationship Between Z-Score Results and Financial Components

Figure 1 presents the correlation matrices for Altman’s Z′- and Z″-Scores with respect to their component financial ratios—liquidity (X₁), retained earnings (X₂), profitability (X₃), and leverage (X₄). These correlations provide valuable insights into the underlying financial drivers influencing the soundness of Sudanese commercial banks between 2016 and 2022.

• Liquidity (X₁) demonstrated a near-perfect positive correlation with both the Z′- and Z″-scores. This indicates that liquidity is the most significant determinant of financial soundness in this context. The banks’ Z-scores increased as liquidity improved, suggesting enhanced capacity to absorb shocks and maintain stability. This finding supports Altman et al. (2017), who emphasized the role of working capital in bank stability during crises.
• Retained earnings (X₂) and profitability (X₃) showed moderate-to-strong positive correlations (approximately 0.49 and 0.54, respectively), suggesting that internal capital generation and operational efficiency significantly contribute to financial health. These results align with Mennawi (2020), who found that internal funding sources were crucial for bank resilience in constrained environments such as Sudan.

Interestingly, leverage (X₄) exhibited a moderate positive correlation (~0.39) with the Z-scores, in contrast to traditional expectations that higher leverage implies greater financial risk. However, within the framework of Sudan’s Islamic banking system, this relationship may reflect the use of equity-based financing instruments such as Mudarabah and Musharakah, which are generally less risky than conventional debt. This nuance aligns with the findings of Beck et al. (2020), who described how Islamic finance models can moderate the risk profile of banks, especially in economies subject to macroeconomic volatility.

These correlations reinforce the notion that liquidity and internally generated capital are primary stabilizers in Sudanese banks. Moreover, they underscore the importance of contextualizing financial analysis within local regulatory, religious, and economic frameworks.

5.1.4. Conclusion for Altman’s Z-Scores Analysis (2016–2022)

This section provided a comprehensive assessment of the financial soundness of Sudanese commercial banks during the pre-conflict period of 2016 to 2022 using Altman’s Z-score models. The application of both Model 3 (Z′) and Model 4 (Z″) reveals a nuanced picture regarding the stability of banks under conditions of escalating macroeconomic stress and political uncertainty.

Between 2017 and 2020, and again in 2022, banks consistently maintained Z-scores above the distress thresholds, signaling a period of relative financial strength. These findings confirm the sector’s resilience and capacity to withstand moderate external shocks, as corroborated by Elhussein and Eldawaha (2024), who noted similar episodes of financial endurance in Sudanese banks.

Conversely, the years 2016 and 2021 marked clear periods of vulnerability, with Z-scores falling into the “Distress” or “Grey” zones. These downturns coincided with intensified economic strain due to international sanctions, hyperinflation, currency devaluation, and the global COVID-19 pandemic. This trend is consistent with the IMF (2020), which linked macroeconomic instability directly to deteriorating bank performance in Sudan.

The correlation analysis further confirmed that liquidity (X₁) was the most critical factor influencing the Z-scores, while retained earnings (X₂) and profitability (X₃) also played supportive roles in maintaining financial health. Although leverage (X₄) typically indicates risk, its positive relationship with the Z-scores in this context reflects Sudan’s unique Islamic banking practices, where leverage is often applied within equity-based, non-interest frameworks, thus reducing its conventional risk implications.

Overall, the results validate the effectiveness of Altman’s models in capturing early warning signs of financial distress and stability in emerging and conflict-affected economies. These findings inform actionable insights for policymakers, regulators, and bank managers seeking to strengthen the sector’s resilience amid continued economic and political instability.

5.1.5. Summary of Altman’s Z-Score Findings

This section provides an analytical summary regarding the soundness of Sudan’s banking sector prior to the outbreak of the civil conflict, covering the period from 2016 to 2022. The Altman’s Z-score results consistently placed most banks within the safe or grey zones, except in years marked by macroeconomic instability and political uncertainty, such as 2016 and 2021. These years saw a decline in profitability, weaker liquidity, and deteriorating capital adequacy ratios, indicating heightened vulnerability. The Z-score findings suggest that, while Sudanese banks demonstrated resilience during stable macroeconomic conditions, their financial buffers are insufficient to absorb sustained conflict-induced shocks without substantial risk of distress.

5.2. Stress Testing Analysis (2022–2028)

5.2.1. Baseline Assessment of Financial Indicators Prior to Conflict

We first established the baseline values for the independent variables—that is, loans, deposits, non-performing loans (NPLs), capital, credit risk, capital risk, and liquidity risk—using the most recent pre-conflict year (2022) as a reference point. These variables collectively reflect the structural soundness and operational capacity of Sudanese commercial banks prior to the outbreak of the civil conflict, thereby forming the foundation for subsequent stress testing analysis.

Data sources and sample: The dataset was compiled from two primary sources. First, audited annual reports of Sudanese commercial banks, as published by the Central Bank of Sudan, provided institution-specific financial data for 2016–2022. Second, country-level macroeconomic and banking sector indicators were sourced from the e-library of the International Monetary Fund (IMF), which also facilitated cross-validation of the reported figures.

The final sample comprised 27 of the 35 licensed commercial banks operating in Sudan, which were selected based on the availability of consistent annual disclosures over the study period. Banks with missing data exceeding two consecutive years were excluded to maintain the dataset’s integrity. All monetary values are presented in Sudanese Pounds (SDG) and standardized to a fiscal year-end of December 31.

Rationale for selecting 2022 as the baseline year: The year 2022 was chosen as the analytical benchmark because it represents the final period of relative macroeconomic stability and full banking operations before the eruption of the 2023 civil conflict. Establishing this baseline ensured that projected deviations in key financial indicators for 2023–2028—under both low- and high-conflict stress scenarios—could be attributed to conflict-induced disruptions rather than pre-existing volatility. This methodological choice is consistent with IMF guidelines for conducting stress testing in fragile and conflict-affected economies.

Grounding the analysis in empirically verified pre-conflict data, this baseline not only captures the prevailing financial structure but also enhances the credibility of the scenario-based projections and the subsequent correlation analysis.

Capital: As of December 2022, the aggregate capital of all Sudanese commercial banks was approximately SDG 1.5 trillion, encompassing paid-up capital and reserves (Central Bank of Sudan, 2022). This nominal figure may not fully capture real value due to inflationary pressures and currency depreciation. Capital components collectively form a buffer against financial risks and underpin the sector’s capacity for lending and investment.

Loans: By December 2022, total loans—or “advances”—issued by the banking sector stood at approximately SDG 1.68 trillion (Central Bank of Sudan, 2022). These loans reflect the cumulative financing extended to various sectors of the economy.

Deposits: As of the same date, Sudanese commercial banks held total deposits of roughly SDG 2.47 trillion, including demand deposits and quasi-money deposits (time and savings accounts) in both domestic and foreign currencies. The substantial share of foreign currency deposits underscores their critical role in the sector, shaped by inflation, currency devaluation, and broader economic uncertainty (IMF, 2022).

Non-performing loans (NPLs): The sector reported an NPL ratio of approximately 4.0% of total loans, equating to about SDG 67.2 billion in December 2022 (Central Bank of Sudan, 2022). The NPL ratio serves as a key indicator of asset quality and credit risk, with implications for banking stability under stress conditions.

5.2.2. Stress Test Modeling Framework

In this part of our study, financial soundness is defined as a multidimensional concept determined by three basic risks—liquidity risk, capital risk, and credit risk—which are seen in this framework as dependent variables. These are determined and affected by many financial indicators, including total capital, total loans, deposits, and non-performing loans, which are seen in this framework as independent variables. Scenario-based simulations were carried out to estimate the responses of all these variables to different stress levels caused by conflict in Sudan from 2022 to 2028.

5.2.3. Low-Conflict Scenario (16.4% Stress Level)

Liquidity Risk Analysis

Under the low-conflict scenario, liquidity stress testing was performed to evaluate the ability of banks to manage short-term funding as the Sudanese conflict continues through 2025. The liquidity risk ratio—measured as the ratio of loans to deposits—increases due to declining depositor confidence and continued lending. As shown in

Table 3. Liquidity risk ratio under low-conflict scenario.

Year	Loans (Billion SDG) ↑	Deposits (Billion SDG) ↓	Liquidity Risk Ratio (LDR) After Stress Test	Liquidity Risk Status
2022	1680	2470	68.02% (pre-stress)	Safe
2023	1955.85	2064.92	94.72%	Tight liquidity
2024	2276.56	1726.27	131.88%	High risk of illiquidity
2025	2649.92	1443.16	183.62%	Critical—liquidity crisis

Source: Authors calculations. ↑ indicates an upward trend in loans; ↓ indicates a downward trend in deposits.

, liquidity risk intensifies steadily, with the ratio rising from 68.02% in 2022 (pre-stress) to 183.62% by 2025. This reflects a troubling shift, as loans are projected to exceed deposits by over 83%, signaling potential insolvency.

Figure 2 visualizes this trend, where deposits decline while loans increase under stress. The sharply rising liquidity risk ratio indicates that by 2025, Sudanese banks may encounter a liquidity crisis if corrective measures are not taken. Such stress underscores the sector’s vulnerability to deposit withdrawal shocks and funding imbalances.

Capital Risk Analysis

Under this scenario, a decline in capital adequacy is projected as capital levels fall and deposit liabilities rise.

Table 4. Capital risk ratio under low-conflict scenario.

Year	Capital (Billion SDG) ↓	Deposits (Billion SDG) ↑	Capital Risk Ratio After Stress Test	Capital Adequacy Status
2022	1500	2470	60.73% (pre-stress)	Very strong
2023	1254.00	2875.08	43.62%	Adequate but weakening
2024	1048.34	3346.59	31.33%	Under-capitalized
2025	876.41	3895.43	22.49%	Critically weak

Source: Authors’ calculations. ↓ indicates a downward trend in capital; ↑ indicates an upward trend in deposits.

indicates a drop from 60.73% in 2022 to 22.49% in 2025—still above the Central Bank of Sudan’s regulatory threshold of 12% but significantly weakened. Figure 3 shows a steep decline in the capital risk ratio, suggesting mounting solvency concerns. Continued exposure to asset deterioration and conflict-driven market disruptions could threaten capital buffers, requiring regulatory action.

Credit Risk Analysis

Credit risk is measured as the ratio of non-performing loans (NPLs) to deposits.

Table 5. Credit risk under low-conflict scenario.

Year	NPLs (Billion SDG) ↑	Deposits (Billion SDG) ↓	Credit Risk Ratio	Credit Risk Status
2022	67.26	2470	2.72% (pre-stress)	Safe, low risk
2023	78.29	2064.92	3.79%	Critical
2024	91.13	1726.27	5.28%	Insolvency warning
2025	106.08	1443.16	7.35%	Systemic failure

Source: Authors’ calculations. ↑ indicates an upward trend in non-performing loans (NPLs); ↓ indicates a downward trend in deposits.

shows a rise from 2.72% in 2022 to 7.35% in 2025, reflecting deteriorating loan performance. However, this level remains below the 10% distress threshold typically used by regulators, indicating moderate resilience. Figure 4 illustrates an upward trend, with worsening macroeconomic conditions contributing to elevated but manageable credit risk levels. This suggests that while asset quality weakens, the sector remains structurally intact under this scenario (Compaoré et al., 2020).

In the low-conflict scenario, Sudanese banks face increasing pressure across all three risk dimensions: liquidity deteriorates sharply due to falling deposits and rising loans, posing short-term solvency threats; capital adequacy declines steadily, although still above regulatory thresholds; and credit risk worsens but remains below systemic thresholds. These findings demonstrate the importance of preventive intervention to preserve financial stability under protracted conflict conditions.

5.2.4. High-Conflict Scenario (25% Stress Level)

Liquidity Risk Analysis

For the high-conflict scenario, it is assumed the Sudanese civil conflict will persist through to 2028, with financial stabilization commencing in 2029. Under this severe stress, liquidity risk escalates dramatically. The loan-to-deposit ratio (LDR), a critical metric of liquidity, rises from 68.02% in 2022 (pre-conflict) to an alarming 1457.81% by 2028 (see

Table 6. Liquidity risk under high-conflict scenario.

Year	Loans (Billion SDG) ↑	Deposits (Billion SDG) ↓	Loan-to-Deposit Ratio (LDR)	Liquidity Risk Status
2022	1680	2470	68.02% (pre-stress)	Stable/liquid
2023	2100	1852.5	113.36%	Warning zone—tight funding
2024	2625	1389.38	188.93%	High risk—liquidity stress
2025	3281.25	1042.04	314.89%	Critical liquidity crisis
2026	4101.56	781.53	524.81%	Systemic stress
2027	5126.95	586.15	874.68%	Near collapse—panic levels
2028	6408.69	439.61	1457.81%	Total breakdown

Source: Authors’ calculation. Source: Authors calculations. ↑ indicates an upward trend in loans; ↓ indicates a downward trend in deposits.

). This indicates that loan volumes will substantially exceed deposit inflows, reflecting a severe imbalance that threatens the sector’s liquidity and the overall financial system’s stability.

This trajectory highlights a near-total erosion of depositor confidence, driven by intensified credit issuance amidst inflationary pressures, currency depreciation, and sharply reduced deposit inflows due to political and economic uncertainty. Figure 5 visually demonstrates this rapid deterioration, with loans increasing sharply as deposits decline, pushing the banking sector toward systemic liquidity distress.

Capital Risk Analysis

Table 7. Capital risk under high-conflict scenario.

Year	Capital (Billion SDG) ↓	Deposits (Billion SDG) ↑	Capital-to-Deposit Ratio (%)	Capital Adequacy Status
2022	1500	2470	60.73% (pre-stress)	Safe zone
2023	1125	3087.50	36.44%	Safe zone
2024	843.75	3859.38	21.86%	Safe zone
2025	632.81	4824.22	13.12%	Warning zone
2026	474.61	6030.28	7.87%	Danger zone
2027	355.96	7537.85	4.72%	Danger zone
2028	266.97	9422.31	2.83%	Danger zone

Source: Authors’ calculations. Source: Authors’ calculations. ↓ indicates a downward trend in capital; ↑ indicates an upward trend in deposits.

documents a marked deterioration in capital adequacy, with the capital-to-deposit ratio declining precipitously from 60.73% in 2022 to a critically low 2.83% in 2028. This trajectory clearly signals an impending solvency crisis, falling well below both the Basel minimum capital requirement of 8% and Sudan’s regulatory threshold of 12%. Although capital levels remain within safe bounds until 2025, the rapid depletion thereafter necessitates urgent recapitalization efforts to avert systemic collapse. Figure 6 illustrates this sharp downward trend, emphasizing escalating financial vulnerability.

Credit Risk Analysis

Credit risk intensifies considerably under prolonged conflict. Initially manageable at 2.72% in 2022, the non-performing loans (NPLs)-to-deposits ratio escalates sharply, surpassing 21% by 2026 and reaching a critical 58.36% in 2028 (see

Table 8. Credit risk under high-conflict scenario.

Year	NPLs (Billion SDG) ↑	Deposits (Billion SDG) ↓	Credit Risk Ratio (%)	Credit Risk Status
2022	67.26	2470	2.72% (pre-stress)	Safe
2023	84.08	1852.5	4.54%	Rising risk
2024	105.09	1389.38	7.56%	Concerning
2025	131.36	1042.04	12.61%	High risk
2026	164.20	781.53	21.01%	Critical
2027	205.25	586.15	35.02%	Severe
2028	256.56	439.61	58.36%	Systemic

Source: Authors’ calculations. ↑ indicates an upward trend in non-performing loans (NPLs); ↓ indicates a downward trend in deposits.

). This progression reflects severe deterioration in asset quality and portends a potential collapse of the loan portfolio. The mounting credit risk necessitates enhanced provisioning and capital buffers to mitigate losses and preserve bank solvency. Figure 7 demonstrates this alarming trend, contrasting declining deposits with rising NPLs.

The high-conflict scenario reveals a profound deterioration across the dimensions of liquidity, capital adequacy, and credit quality: liquidity risk escalates exponentially, signaling an imminent funding crisis; capital buffers erode to critically low levels, necessitating immediate intervention to prevent insolvency; and credit risk simultaneously amplifies sharply, indicating a collapse in asset quality and heightened default risk. Together, these outcomes underscore the fragility of Sudan’s banking sector under protracted conflict and emphasize the need for robust risk management and policy responses.

5.2.5. Conclusion for Stress Testing Analysis (2022–2028)

The stress testing results under both low-conflict (16.4%) and high-conflict (25%) scenarios highlight the profound vulnerability of Sudan’s banking sector to the pressures imposed by the ongoing conflict.

In the low-conflict scenario liquidity risk intensifies substantially, with the loan-to-deposit ratio increasing from 68.02% in 2022 (pre-conflict) to 183.62% by 2025, indicating growing funding challenges. Capital adequacy deteriorates from 60.73% to 22.49% during the same period, remaining above the Central Bank of Sudan’s minimum requirement of 12% but reflecting significant erosion of capital buffers; this is consistent with Elhussein and Eldawaha (2024), who emphasized the fragility of capital under macroeconomic shocks. Credit risk also rises gradually, with the non-performing loans ratio reaching 7.35% by 2028, remaining below critical distress levels but indicating worsening asset quality. These trends are in line with the findings of the IMF (2020), with post-conflict economies, such as South Sudan and Yemen, exhibiting similar patterns of asset quality decline and capital depletion.

Conversely, the high-conflict scenario presents a dire outlook. Liquidity risk becomes unsustainable, as evidenced by the loan-to-deposit ratio soaring beyond 1450% by 2028, signaling severe depositor confidence erosion and potential operational collapse. Capital adequacy ratios plummet to 2.83%—well below regulatory thresholds—while credit risk escalates sharply to 58.36%, indicating a systemic risk of loan portfolio failure. This mirrors findings in conflict-affected contexts as reviewed by Al-Shboul et al. (2020), who linked prolonged political instability to insolvency in the banking sector.

These results align with regional analyses, such as that of Mustafa (2019), who identified the heightened sensitivity of Sudanese banks to political shocks, exacerbated by shallow capital markets, inadequate risk management, and a heavy reliance on public sector lending. Collectively, the reported projections underscore the urgent need for policy interventions including recapitalization, enhanced risk-based supervision, and implementation of the Basel II/III frameworks to stabilize liquidity and mitigate systemic collapse. Proactive regulatory reforms are critical to reducing the fragility of the banking sector, even under severe stress conditions.

5.2.6. Summary of Stress Testing Findings

The stress test results indicate that even under low-intensity conflict, Sudanese banks face increasing liquidity pressure, shrinking capital buffers, and rising credit risk over the forecast horizon. A near-collapse of the banking sector was forecasted under the high-intensity conflict scenario, with liquidity depletion, capital inadequacy, and elevated default probabilities across the sector. These results highlight the systemic fragility of Sudan’s banking system under prolonged armed conflicts, underscoring the urgency of intervention by monetary authorities and international financial institutions.

5.3. Correlation of Risk Factors Across Conflict Scenarios

Table 9. Correlation matrix of key risk factors by conflict scenario.

Risk Factor	Loans	Deposits	NPLs	Capital	Credit Risk	Capital Risk	Liquidity Risk
Loans	1.00	+0.40	+0.55	−0.15	+0.41(L)/+0.56(H)	+0.30	+0.31
Deposits	+0.40	1.00	−0.25	+0.45	+0.50(L)/+0.60(H)	+0.28	−0.18(L)/−0.31(H)
Non-Performing Loans	+0.55	−0.25	1.00	−0.35	+0.62(L)/+0.68(H)	+0.64(L)/+0.65(H)	+0.30
Capital	−0.15	+0.45	−0.35	1.00	−0.22(L)/−0.32(H)	−0.53(L)/−0.50	−0.17
Credit Risk	+0.41(L)/ +0.56(H)	+0.50(L)/ +0.60(H)	+0.62(L)/ +0.68(H)	−0.22(L)/ −0.32(H)	1.00	+0.59(L)/+0.61(H)	+0.35(L)/+0.48(H)
Capital Risk	+0.30	+0.28	+0.64(L)/ +0.65(H)	−0.53(L)/ −0.50	+0.59(L)/+0.61	1.00	+0.44(L)/+0.47
Liquidity Risk	+0.31	−0.18(L)/ −0.31	+0.30	−0.17	+0.35(L)/+0.48(H)	+0.44(L)/+0.47(H)	1.00

Note: Values represent Pearson correlation coefficients. L—low-conflict scenario; H—high-conflict scenario. Empty cells imply symmetry with corresponding variables. Source: Authors’ calculations.

presents the correlation matrix of key financial risk indicators across both low- and high-conflict scenarios for Sudanese commercial banks, illustrating the direction and strength of linear relationships among the core variables: loans, deposits, non-performing loans (NPLs), capital, credit risk, capital risk, and liquidity risk. The correlation coefficients range from −1 to +1, where positive values indicate a direct relationship and negative values reflect an inverse association.

Loans exhibit a moderate positive correlation with deposits (+0.40), suggesting that increased deposits tend to support lending activity. However, loans are also positively associated with NPLs (+0.55), reflecting a proportional rise in asset quality deterioration as credit volume expands. Furthermore, the relationship between loans and credit risk intensifies under high-conflict conditions, rising from +0.41 to +0.56, indicating that lending becomes more hazardous in politically unstable environments (Compaoré et al., 2020). Additionally, loans are modestly correlated with capital risk (+0.30) and liquidity risk (+0.31), suggesting that credit expansion contributes to both systemic vulnerabilities.

Deposits, while positively correlated with capital (+0.45), show negative correlations with NPLs (−0.25) and liquidity risk (−0.18 in low-conflict and −0.31 in high-conflict). The increasing negative correlation under high-conflict scenarios suggests that depositor behavior becomes more sensitive to liquidity pressures and asset quality during periods of instability. Deposits also exhibit stronger positive correlations with credit risk under the high-conflict scenario (+0.60 vs. +0.50), indicating that larger deposit bases may be funding riskier credit portfolios in such settings.

Non-performing loans (NPLs) demonstrate strong positive correlations with credit risk (+0.62/+0.68) and capital risk (+0.64/+0.65), reinforcing the notion that worsening asset quality undermines both earnings and solvency buffers. NPLs are also inversely correlated with capital (−0.35), consistent with international evidence on the erosion of bank capital under non-performing asset stress (IMF, 2020).

Capital is negatively correlated with nearly all other risk factors—particularly credit risk (−0.22/−0.32) and capital risk (−0.53/−0.50)—affirming its sensitivity to deteriorating credit and solvency conditions. Its positive correlation with deposits confirms the supportive role of stable funding in maintaining capital adequacy.

Overall, the transition from low- to high-conflict conditions is marked by stronger and more adverse correlations across variables, indicating increasing systemic fragility. As the conflict escalates, the relationships between core risk indicators become more pronounced, which is indicative of a banking sector exposed to compounding vulnerabilities and a heightened likelihood of sector-wide instability. These findings reinforce the stress testing results and highlight the need for enhanced supervisory frameworks, Basel III implementation, and counter-cyclical capital buffers to reduce inter-risk contagion in fragile states (Compaoré et al., 2020).

Correlation Matrix by Conflict Scenario (Low- vs. High-Conflict Levels)

Figure 8 illustrates the correlation coefficients among key risk factors—namely, loans, deposits, non-performing loans (NPLs), capital, credit risk, capital risk, and liquidity risk—under the two conflict stress scenarios: low-intensity (16.4%) and high-intensity (25%); in particular, the left map represents correlations under the low-conflict scenario, while the right visualizes changes under high-conflict conditions.

Under the low-conflict scenario, moderately positive correlations are observed between loans and NPLs (+0.55) and between deposits and capital (+0.45), suggesting that some risk dynamics remain under control. However, early signs of stress appear in the negative correlation between NPLs and capital (−0.35), indicating that asset quality deterioration is already beginning to erode capital buffers.

Under the high-conflict scenario, correlations intensify and become more destabilizing. The link between NPLs and credit risk strengthens (+0.68), reflecting the deepening crisis in asset quality. Moreover, a sharper negative correlation between deposits and liquidity risk (−0.31) suggests a loss of depositor confidence and growing illiquidity. The inverse relationship between capital and credit risk (−0.32) also widens, implying that capital bases erode further as credit portfolios weaken.

These correlation patterns indicate escalating systemic risk under prolonged conflict, highlighting the need for proactive macroprudential and stress-sensitive regulatory measures.

6. Findings, Limitations, and Recommendations

This study assessed the financial resilience of Sudanese commercial banks using Altman’s Z-score models (Models 3 and 4) and forward-looking stress testing for the period 2016–2022, with projections through to 2028. This combined methodology highlighted systemic risks amid ongoing conflict.

Empirical results obtained with the Z′- and Z″-score models revealed that banks maintained relative stability in 2017–2020 and 2022, with scores largely above distress thresholds. Periods of heightened vulnerability occurred in 2016 and 2021, coinciding with macroeconomic disruptions such as sanctions, hyperinflation, currency depreciation, and the COVID-19 pandemic. Liquidity emerged as the most critical driver of financial soundness, followed by retained earnings and profitability. Interestingly, leverage displayed a moderately positive correlation with the Z-scores—a finding consistent with Sudan’s Islamic finance structure, where profit-sharing contracts mitigate conventional debt risk.

Stress testing confirmed severe fragility under both low-conflict (16.4%) and high-conflict (25%) scenarios. Even under moderate conflict, liquidity pressures escalated, capital adequacy declined from 60.73% to 22.49%, and credit risk more than doubled by 2025. Meanwhile, under the high-conflict scenario a near-total systemic breakdown was projected by 2028, with loan-to-deposit ratios exceeding 1450%, capital buffers collapsing to 2.83%, and credit risk rising above 58%—conditions that would render most banks insolvent. Correlation analysis further showed that risk interdependencies intensify with conflict duration, amplifying systemic instability.

These findings confirm that Altman’s models remain effective early-warning tools for the identification of vulnerabilities in conflict-affected banking sectors, and that their predictive accuracy is enhanced when combined with scenario-based stress testing.

6.1. Summary of Key Findings

• Validation of Altman Z-score models—Both Models 3 and 4 accurately captured shifts in financial stability and distress signals in Sudanese banks, reinforcing their applicability in emerging markets under political instability.
• Liquidity as a primary determinant—Strong positive correlations between liquidity and the Z-scores underscore the centrality of liquidity management for resilience.
• Distinct leverage effects in Islamic banking—Positive leverage–Z-score correlations suggest that equity-based financing instruments, such as Mudarabah and Musharakah, mitigate traditional leverage risk.
• Conflict duration–risk escalation nexus—A longer conflict duration was shown to correlate with liquidity depletion, capital erosion, and rising credit risk, especially beyond 2028 under the high-conflict scenario.
• Stress testing alignment with historical patterns—Scenario analysis confirmed that even moderate conflicts can rapidly destabilize liquidity, capital, and asset quality.

6.2. Limitations

While the study offers valuable insights, several limitations warrant acknowledgment:

• Data Availability and Reliability: Publicly reported financial data may be incomplete or delayed, particularly under conflict conditions.
• Model Scope and Assumptions: Altman’s Z-Score models are accounting-based and may not fully capture forward-looking risks or off-balance-sheet exposures.
• Sectoral Concentration in Islamic Banking: Positive leverage–Z-score correlations may not generalize across banks with concentrated sectoral exposures.
• Political and Macroeconomic Uncertainties: Projections beyond 2028 assume relative macroeconomic stability; unexpected global shocks could affect outcomes.

6.3. Future Research Directions:

• Integrate market-based risk measures (e.g., CDS spreads, interbank rates).
• Conduct bank-level behavioral analysis to capture governance and decision-making effects.
• Expand comparative studies to other fragile and conflict-affected states.
• Develop scenario expansions incorporating technological and cross-border contagion risks.
• Assess the impact of post-conflict regulatory reforms, including Basel III implementation.

6.4. Recommendations

Based on the findings and stress-testing insights, the following recommendations are proposed:

• Prioritize Conflict Resolution for Financial Recovery: Sustainable stability in Sudan’s banking sector critically depends on ending the civil conflict. Post-conflict priorities should include restoring liquidity flows, recapitalizing banks, and re-establishing effective central bank oversight.
• Strengthen Prudential Regulation and Supervision: The Central Bank of Sudan should enforce capital adequacy and liquidity requirements more rigorously. Dynamic bank risk profiling and regular conflict-sensitive stress tests should be institutionalized to anticipate and mitigate systemic risk.
• Mandate Crisis Preparedness and Contingency Planning: Banks should maintain contingency reserves and crisis management plans to address conflict-driven risks such as sudden liquidity shocks, capital flight, and surges in non-performing loans (NPLs). Stress-testing exercises should be updated regularly to incorporate emerging conflict scenarios.
• Leverage Predictive Analytics in Supervisory Frameworks: Embedding Altman’s Z-Score models, along with scenario-based stress testing, into regulatory monitoring enhances early-warning capabilities. Predictive analytics can guide targeted interventions before systemic vulnerabilities crystallize.
• Institutional Capacity Building and Governance Enhancement: Collaboration between regulators, policymakers, and international partners is essential to improve technical expertise, governance practices, and risk management capabilities. Training programs should focus on conflict-sensitive financial oversight, liquidity risk management, and crisis response.
• Implement Basel III Standards and Risk-Based Supervision: Counter-cyclical capital buffers, liquidity coverage ratios, and enhanced risk-based supervision can reduce interbank contagion and strengthen systemic resilience. The Basel III framework should be adapted to Sudan’s unique economic and Islamic banking context, including allowances for equity-based financing instruments.
• Monitor Conflict Duration–Risk Nexus Continuously: Given the direct relationship between conflict duration and systemic fragility, regulators should continuously monitor macroeconomic and conflict indicators to adjust stress-test assumptions. Scenario planning should consider both moderate (16.4%) and severe (25%) stress levels, reflecting empirical evidence from the IMF and other conflict-affected states.