Open research questions in Financial Distress and Bankruptcy Prediction

What the literature leaves open

• 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 industries

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

• Further research could explore the application of symmetric probabilistic models in other industries - Further research could investigate the use of other regularization techniques

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

• This is a dual-model approach designed to bridge this important gap in the literature with actionable insights on the vulnerabilities of a sector vital for both economic stability and food security for the nation of Colombia.

  Predicting financial distress in the food production sector: a dual-model approach using Z-score and O-score methods · 2025 · DOI

• The lack of effective systems for detecting fraudulent transactions and assessing credit risk in fintech platforms. The inability of traditional rule-based systems to keep up with the pace of transactions in fintech platforms.

  Leveraging Machine Learning for Real-Time Fraud Detection and Risk Assessment in Modern Fintech Platforms Aayush Bharat Mandavia and Anurag Shrivastava · 2026 · DOI

• The evaluation period for fraud detection is 6 months and for credit risk is 12 months, but the paper does not discuss performance stability beyond these windows or how concept drift and temporal changes affect model reliability.

  Leveraging Machine Learning for Real-Time Fraud Detection and Risk Assessment in Modern Fintech Platforms Aayush Bharat Mandavia and Anurag Shrivastava · 2026 · DOI

• Client drift caused by heterogeneous portfolios and non-IID accounting distributions. High gradient noise induced by rare defaults and heavy-tailed features. The need for communication-efficient methods that can maintain privacy constraints.

  FedQuAD: Fast-Converging Curvature-Aware Federated Learning for Credit Default Prediction from Private Accounting Data · 2026 · DOI

• Further research can explore the application of FedQuAD to other domains, - Investigating the use of FedQuAD with other types of machine learning models

  FedQuAD: Fast-Converging Curvature-Aware Federated Learning for Credit Default Prediction from Private Accounting Data · 2026 · DOI

• The existing literature has not conclusively demonstrated the superiority of ML methods over traditional approaches such as LR in bankruptcy prediction. There is a need to evaluate the predictive performance of ML methods in the context of SMEs.

  AI-Driven Bankruptcy Prediction in Manufacturing SMEs: Comparing Machine Learning Techniques with Logistic Regression · 2026 · DOI

• The paper identifies a research gap in the application of AI and ML in financial services. The paper notes that there is a need for further research on the challenges and limitations of adopting AI and ML in financial services. The paper highlights the importance of considering ethical issues and risks associated with data privacy and security in the use of AI and ML in financial services.

  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. International Journal of Computational Intelligence Systemshttps://doi.org/10.1007/s44196-025-01110-01 3 Page 17 of 21 181 17 The coming few years are likely to witness a significant speedup in the adoption of AI and ML technology across financial institutions. A major forecast is that AI will increasingly become integrated into financial risk management. According to Khanday et al. (2025), the potential of AI to transform risk management is its capacity to analyze market conditions in real-time, thus allowing financial institutions to anticipate and counteract incoming threats in a timely manner. The increasing maturity of predictive analytics will enable banks to make data-driven, informed decisions that improve their risk avoidance measures and operations efficiency. Also, customer service personalization is expected to become a signature feature of financial services with the use of AI. Research (Aithal and Prabhu 2025), point out that banks will use sophisticated ML models to scan through tremendous amounts of customer data to enable them to personalize products and services based on individual tastes and actions. This move towards hyper-personalization will be expected to vastly enhance customer satisfaction and loyalty since clients are being offered products that suit their specific requirements. With continued advances in AI, the banking industry will also witness a departure from universal solutions towards more personalized experiences. In addition, AI capabilities will be used more widely in regulatory compliance. Because complex financial regulation will require novel means of meeting compliance requirements, AI can automate many aspects of compliance monitoring to help adhere to legal obligations more efficiently and effectively. The existing research (Singh et al. 2023), explained that this will also allow institutions to traverse the challenging regulatory environment while decreasing the risks of non-compliance risk. In the context of AI/ML in financial services, there are several important areas to explore for future research and development. One key area is the ethical implications of AI, and in particular the issue of bias in algorithmic decision making. In our view, it is vital to address the ethical issues associated with AI usage, especially in terms of bias in fostering fair, transparent and accountable AI applications.

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

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

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

• Dependence on traditional financial institutions. 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

• Dependence on third-party software and technological infrastructures. Exposure to new vulnerabilities such as cyber risk, data privacy, and platform monopoly. 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 lack of transparency in complex machine learning models for credit risk prediction. The need for a balanced trade-off between predictive accuracy and model interpretability. The importance of explainable AI in financial risk modeling.

  Explainable Machine Learning for Credit Risk Prediction Using Lightweight Models: A Comparative Study of Accuracy-Interpretability Trade-offs · 2026 · DOI

• Rising costs and repayment obligations. The need for cost control and strengthening reserves. The importance of aligning growth strategies with market conditions.

  Financial Forecasting & Prediction of Financial Distress · 2026 · DOI

• To empirically validate the proposed framework in real-world scenarios. To explore the application of the framework in other risk-sensitive fields. To investigate the use of other machine learning algorithms and optimization techniques.

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

• The lack of transparency and interpretability in traditional credit risk assessment models. The need for a framework that can provide accurate and transparent credit risk assessments in digital lending ecosystems.

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

• The inherent ambiguity and rapid change in financial data make conventional rule-based methods employed in financial auditing ill-equipped. The lack of a novel framework that incorporates quantitative financial data with qualitative expert opinion to dynamically prioritize audit actions.

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

• of traditional methods. The financial data-mining method proposed by Li et al. [19] employs fuzzy clustering to enhance risk assessment and early warning. This method groups financial data with ill-defined boundaries and extracts relevant patterns for predictive analysis via fuzzy clustering. This technique, which accounts for the imprecision and ambiguity of financial facts, overcomes the limitations of crisp clustering. The method employs multiple financial indicators, some imprecise, to enhance the reliability and precision of financial risk assessment. Liu et al. [20] studied information disclosure and financial data mining using fuzzy logic. This ARTICLE IN PRESS ARTICLE IN PRESS research simplifies the analysis of large financial datasets with missing or ambiguous data by employing fuzzy set theory. The algorithms improve data handling, allowing for more precise data extraction for regulatory compliance, financial reporting transparency, and risk management. The research shows that fuzzy logic can reduce ambiguity in financial data and provide deeper insights into financial disclosures. By combining qualitative expert opinions with quantitative financial measures, the FAHP method takes a revolutionary approach. The combination generates fuzzy weights for real-time audit decision-making. There are few published solutions that prioritize risk-based audit prioritizing and optimize resources in a changing financial environment. This is true even when audit systems use AI and process mining to examine transactions and investigate fraud. The paper proposes using FAHP to prioritize audit jobs by fuzzy-ranked financial concerns boosts audit efficiency and accuracy. It is a major improvement over prior methodologies, which often used predefined criteria or ignored financial audits' dynamic nature. The FAHP-based framework is being promoted as a new auditing tool that integrates financial experts' perspectives with real-time, risk-based financial- sector data. The method improves audit accuracy, resource distribution, and adaptability. The framework challenges traditional and AI-based financial audits with an efficient, interpretable, and adaptable alternative. The novel aspect of the proposed method is its adaptability across applications; it integrates quantitative financial data with qualitative expert opinion within a framework that can be customized to each case. Furthermore, it employs FAHP (Fuzzy Analytic Hierarchy Process) to rank audits in real time based on the risk they represent. This approach uses fuzzy logic to convert financial ratios and expert opinions into fuzzy weights. This is markedly different from the usual auditing solutions that rely on preset criteria or thresholds, or on AI-based models that require frequent retraining. This enables the system to respond promptly to changing financial conditions and emerging threats. Auditors will be able to trace each decision through to the fuzzy logic computations, since the system's decision-making process is interpretable and transparent. The approach provides a more adaptable, interpretable, and resource-optimized solution for current financial audits by allowing for flexible audit planning and focusing resources on the most severe financial challenges. This significantly improves the efficacy, precision, and efficiency of audits.

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

• Traditional static risk control models have significant limitations in capturing intricate correlation risks and contagion effects. There is a need for a novel integrated risk control framework that integrates dynamic network topology analysis with macroeconomic contextual factors.

  MCATSA: Multi-Strategy Collaborative Adaptive Tree-Seed Algorithm and Its Application in Dynamic Credit Risk Assessment · 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

Most-cited papers in Financial Distress and Bankruptcy Prediction

• Credit rating analysis with support vector machines and neural networks: a market comparative study · Decision Support Systems · 2003 · 753 citations
• Managerial Applications of Neural Networks: The Case of Bank Failure Predictions · Management Science · 1992 · 742 citations
• The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification · BioData Mining · 2023 · 556 citations
• Screening Peers Softly: Inferring the Quality of Small Borrowers · Management Science · 2015 · 552 citations
• A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers · International Journal of Forecasting · 2000 · 549 citations
• Bankruptcy prediction using neural networks · Decision Support Systems · 1994 · 483 citations
• Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress · The Journal of Finance · 1985 · 456 citations
• Predictably Unequal? The Effects of Machine Learning on Credit Markets · The Journal of Finance · 2021 · 430 citations
• Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending · Applied Economics · 2014 · 404 citations
• Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation · Management Science · 2003 · 376 citations

Most recent work

• Enhancing audit quality and reducing costs: the impact of AI in banking and financial services · Frontiers in Artificial Intelligence · 2026
• Unveiling the impact of artificial intelligence on corporate misconduct, the perspective of information asymmetry · Technological Forecasting and Social Change · 2026
• Digital twin-enhanced credit risk prioritization in mortgage portfolios: a hybrid model approach · Quality & Quantity · 2026
• Multi-class financial distress prediction using the textual information of earnings communication conferences based on ensemble machine learning models · Journal of Business Research · 2026
• Bidirectional Relationship Between Corporate Social Responsibility and Financial Distress of Firms in Emerging African Countries: Evidence From Wavelet Enhanced QQR Models · Corporate Social Responsibility and Environmental Management · 2026
• Confidence-scaled margin adaptation boosting for interpretable financial distress prediction · International Journal of Forecasting · 2026
• FedQuAD: Fast-Converging Curvature-Aware Federated Learning for Credit Default Prediction from Private Accounting Data · Mathematics · 2026
• Uncovering cross-organizational risk patterns: a machine learning approach to predicting financial fraud via chain leader attributes · Review of Managerial Science · 2026
• Enhanced predictive modeling for financial risk assessment using hybrid AI (ML & DL) on structured and unstructured data · Discover Artificial Intelligence · 2026
• A spatio-temporal machine learning model for mortgage credit risk: Default probabilities and loan portfolios · European Journal of Operational Research · 2026

Related topics in Business, Management and Accounting