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Open research questions in Financial Distress and Bankruptcy Prediction

158 gap statements mined from Financial Distress and Bankruptcy Prediction papers in our 7.9M-paper local library, which holds 1,084 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The need to balance model predictability and interpretability in predicting construction company failure.

    Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends · 2024 · DOI

  • These limitations suggest that future research should explore more standardized empirical designs and further investigate the integration of financial, qualitative and institutional variables in insolvency prediction models, particularly in SME context.

    Understanding corporate insolvency in Portugal: concepts, regulatory framework and analytical approaches · 2026 · DOI

  • The study, on the other hand, only focused on some banks in the biggest city in the south of Vietnam, so further research on the area is needed, such as over Vietnam.

    Factors Affecting the Ability to Repay Debts of Corporations at Commercial Banks · 2024 · DOI

  • Future research should focus on exploring the intersection of AI with regulatory technologies, enhancing compliance processes, and investigating the ethical implications of algorithmic decision-making.

    Operational Excellence Through AI and ML in Financial Services: A Comprehensive Review of Applications, Challenges, and Future Directions · 2026 · DOI

  • There is a lot of momentum in combining AI and ML with financial services. Changes are coming quickly with the potential for substantial impact on the industry. In this section, predictions about future transitions of AI/ML in financial services are explored, opportunities for future research and innovation are identified, and suggestions for stakeholders to prepare for future changes are offered.

    Operational Excellence Through AI and ML in Financial Services: A Comprehensive Review of Applications, Challenges, and Future Directions · 2026 · DOI

  • Need for robust data privacy and cyber measures to protect SMEs' sensitive data.

    Re-Evaluation of Resource Dependence in AI Enabled SME Financing · 2026 · DOI

  • The gap the paper identifies is the need to study the impact of AI on automated financial reporting and analysis.

    Impact of Artificial Intelligence (AI) on Automated Financial Reporting and Analysis · 2026 · DOI

  • The need to investigate financial distress and its associated challenges and opportunities in manufacturing firms in Ethiopia. The lack of understanding of strategic responses to financial pressure and opportunities for improvement.

    A descriptive analysis of financial distress patterns in manufacturing firms: challenges, financing constraints, opportunities, and strategic responses in an emerging economy · 2026 · DOI

  • It will pursue external validation across sectors and regulatory compliance gaps to verify scalability, robustness, and general applicability across different financial and operational environments.

    Fuzzy algorithm-driven financial statement analysis and intelligent audit system design · 2026 · DOI

  • Further research can be conducted to compare the proposed model with other machine learning models. The study's findings can be applied to other domains with similar challenges.

    Integration of Stacking Ensemble and Explainable AI for Taxpayer Compliance Risk Profiling · 2026 · DOI

  • The lack of tangible data on corporate tax avoidance makes it challenging to develop accurate prediction models. The extreme class imbalance in tax administration datasets is a significant challenge.

    Integration of Stacking Ensemble and Explainable AI for Taxpayer Compliance Risk Profiling · 2026 · DOI

  • There is a need to study the performance of Islamic banks in Indonesia using EVA and financial distress models. The existing literature lacks an analysis of the capital structure of Islamic banks in Indonesia.

    ANALISIS KINERJA DENGAN METODE ECONOMIC VALUE ADDED (EVA ) DAN FINANCIAL DISTRESS SUATU STUDI PADA PT BANK UMUM SYARIAH · 2026 · DOI

  • Traditional credit scoring methods have limitations and often fail to capture complex borrower behavior. Many AI-based credit scoring systems lack robust identity verification mechanisms. Existing systems often ignore or inadequately integrate bureau information, which compromises the reliability of the generated credit scores.

    AI-Based Credit Scoring System · 2026 · DOI

  • The gap in the literature is the lack of studies that incorporate machine learning and credit risk assessment into a formalized risk management and econometric assessment system. Prior studies have focused on structural and reduced-form credit risk models, but have not explored the use of deep learning models.

    Deep Learning in Credit Risk Assessment: A Data-Driven Approach to Transforming Financial Decision-Making and Risk Analytics · 2026 · DOI

  • Traditional models may not adequately capture the complex and nonlinear dynamics of financial degradation. The need for a more nuanced understanding of credit deterioration and its relationship with financial variables.

    Do Complex Models Matter? Evidence from Multiclass Machine Learning Models in Credit Outlook Prediction · 2026 · DOI

  • This finding also resonates with Simon's (1957) theory of bounded rationality, which posits that decision makers are constrained by limited information and cognitive capacity.

    Artificial intelligence and financial decision making in Indian banks: Adoption, effectiveness, and challenges · 2026 · DOI

  • Failure is fast, while recovery is slow. A brief liquidity shortfall can cause permanent structural damage. Conventional risk models miss key findings, such as the asymmetry of failure and recovery. The need to model saturation and depletion in supply chain dynamics.

    The Solvency Margin: A Speed-Limit Metric for Capital-Constrained Organizations Under Stress · 2026 · DOI

  • The gap in the literature is the limited comparison of different methods for predicting business failure. The comparison of PLS with other methods is particularly limited.

    Prévision de la défaillance des entreprises : comparaison de l'analyse discriminante, la régression logit et PLS · 2026 · DOI

  • Limited access to long-term capital. Exposure to new vulnerabilities such as cyber risk, data privacy, and platform monopoly.

    Re-Evaluation of Resource Dependence in AI Enabled SME Financing · 2026 · DOI

  • Evaluation metrics Accuracy is the fraction of correctly classified instances in all the evaluated samples. It indicates the overall performance of the model across all classes. However, despite its popularity, it can be misleading when applied to an unbalanced dataset.

    Transforming credit risk evaluation in digital lending from black box models to transparent decisions · 2026 · DOI

  • The benchmark comparison reported here is restricted to the FY2022 banking panel. The failed banks in this panel provide a clean binary classification target that aligns with what the established classifiers were designed to predict. The simulation scale was limited to 500,000+ simulations.

    The Solvency Margin: A Speed-Limit Metric for Capital-Constrained Organizations Under Stress · 2026 · DOI

  • Limited analysis of financial ratios on financial distress prediction in pharmaceutical companies. Limited comparison of the predictive ability of the Grover, Ohlson, and Zavgren models.

    Pengaruh Rasio Keuangan terhadap Prediksi Financial Distress dengan Model Grover, Ohlson, dan Zavgren pada Perusahaan Farmasi yang Terdaftar di Bursa Efek Indonesia (BEI) Periode 2020-2024 · 2026 · DOI

  • By providing empirical evidence for the mitigating effect of AI innovation on debt default risk of construction firms, this study fills a critical gap in the literature of AI innovation and construction risk management.

    Impact of artificial intelligence innovation on debt default risk of construction firms: the moderating role of intellectual capital · 2025 · DOI

  • Traditional credit scoring models have limitations, - AI-based credit scoring systems can analyze large datasets and identify patterns in borrower…

    Impact of AI-Based Credit Scoring on Loan Risk Management in Indian Banks · 2026 · DOI

  • The dataset is cross-sectional and limited to a single year - The dataset exhibits a moderate class imbalance - The use of homogeneous sector-specific data reduces variability caused by structural differences across…

    Symmetry-Based Comparison of Logit and Probit Models for Financial Distress Prediction in the Automotive Industry · 2026 · DOI

Most-cited papers in Financial Distress and Bankruptcy Prediction

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Open questions raised by papers listed on this page include: (1) Future research should focus on exploring the intersection of AI with regulatory technologies, enhancing compliance processes, and investigating the ethical implications of algorithmic decision-making (“Operational Excellence Through AI and ML in Financial Services: A Comprehensive…”, 2026); (2) The lack of understanding of strategic responses to financial pressure and opportunities for improvement (“A descriptive analysis of financial distress patterns in manufacturing firms:…”, 2026); (3) The lack of tangible data on corporate tax avoidance makes it challenging to develop accurate prediction models (“Integration of Stacking Ensemble and Explainable AI for Taxpayer Compliance…”, 2026); (4) There is a need to study the performance of Islamic banks in Indonesia using EVA and financial distress models (“ANALISIS KINERJA DENGAN METODE ECONOMIC VALUE ADDED (EVA ) DAN FINANCIAL…”, 2026); (5) The gap in the literature is the lack of studies that incorporate machine learning and credit risk assessment into a formalized risk management and econometric assessment system (“Deep Learning in Credit Risk Assessment: A Data-Driven Approach to Transforming…”, 2026). 158 gap statements have been mined from Financial Distress and Bankruptcy Prediction papers in our 7.9M-paper local library, which holds 1,084 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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