Articles

Bankruptcy Prediction Models for SMEs Using Machine Learning and Auditing Standards: An Integrated Early-Warning and Assurance Framework

Bankruptcy Prediction Models for SMEs Using Machine Learning and Auditing Standards: An Integrated Early-Warning and Assurance Framework

Burhan Reshat Rexhepi

Abstract

Small and medium-sized enterprises (SMEs) account for a large share of employment and credit portfolios, yet their failure dynamics are difficult to predict due to heterogeneous reporting quality, limited disclosures, and non-linear distress pathways. This paper proposes an integrated framework that combines machine-learning (ML) bankruptcy prediction with audit-orientated evidence requirements aligned with International Standards on Auditing (ISAs). It synthesises classical models (Altman-type discriminant analysis and Ohlson-type logistic regression) with modern ensemble learners (Random Forest and gradient boosting/XGBoost) and maps model outputs to the auditor’s structured responsibilities: risk assessment (ISA 315 Revised 2019), responses to assessed risks (ISA 330), fraud considerations (ISA 240 Revised), and going concern evaluation (ISA 570 Revised 2024). Results indicate that ML can improve discrimination under class imbalances and complex interactions, but only when governance controls prevent data leakage, model drift, and “automation bias”. The paper concludes with an implementable early-warning and assurance blueprint for banks and audit practices in candidate-country contexts.

How to Cite

Rexhepi, B. R. (2026). Bankruptcy Prediction Models for SMEs Using Machine Learning and Auditing Standards: An Integrated Early-Warning and Assurance Framework: Bankruptcy Prediction Models for SMEs Using Machine Learning and Auditing Standards: An Integrated Early-Warning and Assurance Framework. Transnational Academic Journal of Economics, 3(1). Retrieved from https://tacje.net/index.php/pub/article/view/77
Endnote/Zotero/Mendeley (RIS) BibTeX

References

  1. Amirshahi, A., & Lahmiri, S. (2024). Review and empirical assessment of ensemble learning for bankruptcy prediction under class imbalance. Expert Systems with Applications, 252, 123–137.
  2. Barboza, F., Kimura, H., & Altman, E. I. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417.
  3. Biecek, P., & Burzykowski, T. (2021). Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. CRC Press.
  4. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
  5. Cao, Y., Li, Y., & Zhang, H. (2024). The dilemma of accuracy in bankruptcy prediction: An approach using explainable AI techniques. European Journal of Innovation Management, 28(11), 1–25.
  6. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). ACM.
  7. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
  8. DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated ROC curves. Biometrics, 44(3), 837–845.
  9. EBA (European Banking Authority). (2023). Follow-up report on machine learning for IRB models. European Banking Authority.
  10. EBA (European Banking Authority). (2021). Guidelines on loan origination and monitoring (EBA/GL/2020/06). European Banking Authority.
  11. European Commission. (2013). Directive 2013/34/EU on annual financial statements, consolidated financial statements and related reports. Official Journal of the European Union.
  12. European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act). Official Journal of the European Union (EUR-Lex).
  13. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
  14. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
  15. Gárate, A., Martínez, J., & Salazar, A. (2024). Bankruptcy prediction using XGBoost and variable selection: Evidence from firm-level financial ratios. Decision Support Systems, 179, 114–132.
  16. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  17. Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541.
  18. He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
  19. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
  20. IAASB (International Auditing and Assurance Standards Board). (2019). ISA 315 (Revised 2019): Identifying and assessing the risks of material misstatement. IFAC.
  21. IAASB (International Auditing and Assurance Standards Board). (2025). ISA 570 (Revised 2024): Going Concern. IFAC. (Effective for periods beginning on/after 15 Dec 2026.)
  22. IAASB (International Auditing and Assurance Standards Board). (2025). ISA 240 (Revised): The auditor’s responsibilities relating to fraud in an audit of financial statements. IFAC. (Effective for periods beginning on/after 15 Dec 2026.)
  23. IAASB (International Auditing and Assurance Standards Board). (2020). ISA 330: The auditor’s responses to assessed risks. IFAC.
  24. ISO. (2019). ISO 31000: Risk management—Guidelines. International Organization for Standardization.
  25. Jensen, R., & Madsen, D. Ø. (2022). Continuous auditing and continuous monitoring: A review and agenda. Journal of Emerging Technologies in Accounting, 19(2), 1–20.
  26. Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1–2), 3–34.
  27. Kim, Y., Kang, M., & Park, J. (2023). Corporate bankruptcy prediction using explainable AI (SHAP): Practical implications for interpretability. Journal of Intelligence and Information Systems, 29(4), 55–74.
  28. Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring. European Journal of Operational Research, 247(1), 124–136.
  29. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems.
  30. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). The M5 accuracy competition: Results and findings. International Journal of Forecasting, 38(4), 1346–1364.
  31. Molnar, C. (2022). Interpretable Machine Learning (2nd ed.). Leanpub.
  32. NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology.
  33. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
  34. OCC (Office of the Comptroller of the Currency). (2011). OCC Bulletin 2011-12: Sound practices for model risk management. OCC.
  35. Platt, J. (1999). Probabilistic outputs for support vector machines. In A. Smola et al. (Eds.), Advances in Large Margin Classifiers (pp. 61–74). MIT Press.
  36. Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC and informedness. Journal of Machine Learning Technologies, 2(1), 37–63.
  37. Reuters. (2024). Banks told to anticipate risks from using AI and machine learning. Reuters.
  38. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD (pp. 1135–1144). ACM.
  39. Son, H., Hyun, C., & Kim, S. (2019). Machine learning-based corporate failure prediction: A practical review for deployability. Sustainability, 11(18), 1–21.
  40. Springer. (2025). An experimental survey of imbalanced learning algorithms for bankruptcy prediction. Artificial Intelligence Review, 58, 1–35.
  41. Srivastava, R. P., & Kogan, A. (2022). Audit analytics, AI, and the transformation of audit judgment: A research synthesis. International Journal of Accounting Information Systems, 45, 100–120.
  42. Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192.
  43. Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit Scoring and Its Applications. SIAM.
  44. Tsai, C.-F., & Hsu, Y.-F. (2021). Corporate distress prediction using hybrid feature selection and ensemble learning. Decision Support Systems, 145, 113–125.
  45. Wang, G., Ma, J., & Yang, S. (2014). An improved boosting-based approach for corporate bankruptcy prediction. Expert Systems with Applications, 41(17), 8343–8355.
  46. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.
  47. Wu, Y., & Olafsson, S. (2023). Model monitoring and drift detection for tabular ML in regulated decisioning. Journal of Risk Model Validation, 17(2), 45–72.
  48. Zięba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in bankruptcy prediction. Expert Systems with Applications, 58, 93–101.
  49. Zhang, D., & Zhou, L. (2024). Tri-XGBoost improved by Borderline-SMOTE and ENN: An interpretable semi-supervised approach to bankruptcy prediction. Knowledge and Information Systems, 66, 1–28.
  50. Zhou, Y., Fu, Z., & Yang, X. (2024). Bankruptcy prediction using machine learning and SHAP: Evidence from large-scale firm samples. Annals of Operations Research, 334, 1–29.
  51. Decision Support Systems Editorial Board. (2023). Special issue on explainable AI in financial risk. Decision Support Systems, 170, 113–118.
  52. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
  53. European Central Bank. (2024). Supervisory expectations for model governance and the use of machine learning in internal models (supervisory guidance note). ECB Banking Supervision.
  54. The Institute of Internal Auditors. (2022). GTAG 3 (2nd ed.): Continuous auditing—Coordinating continuous auditing and monitoring to provide continuous assurance. IIA.
  55. Wiley Online Library. (2024). Identifying going concern audit opinions using supervised machine learning. International Journal of Auditing, 28(4), 1–18.
  56. Murtezaj, I. M., Rexhepi, B. R., Dauti, B., & Xhafa, H. (2024). Mitigating economic losses and prospects for the development of the energy sector in the Republic of Kosovo. Economics of Development, 23(3), 82–92. https://doi.org/10.57111/econ/3.2024.82
  57. Rexhepi, B. R., Mustafa, L., Sadiku, M. K., Berisha, B. I., Ahmeti, S. U., & Rexhepi, O. R. (2024). The impact of the COVID-19 pandemic on the dynamics of development of construction companies and the primary housing market. Architecture Image Studies, 5(2). https://doi.org/10.48619/ais.v5i2.988
  58. Daci, E., & Rexhepi, B. R. (2024). The role of management in microfinance institutions in Kosovo: Case study Dukagjini Region. Quality – Access to Success, 25(202). https://doi.org/10.47750/QAS/25.202.22
  59. Murtezaj, I. M., Rexhepi, B. R., Xhaferi, B. S., Xhafa, H., & Xhaferi, S. (2024). The study and application of moral principles and values in the fields of accounting and auditing. Pakistan Journal of Life and Social Sciences, 22(2), 3885–3902. https://doi.org/10.57239/PJLSS-2024-22.2.00286
  60. Rexhepi, B. R., Rexhepii, F. G., Xhaferi, B., Xhaferi, S., & Berisha, B. I. (2024). Financial accounting management: A case of Ege Furniture in Kosovo. Quality – Access to Success, 25(200). https://doi.org/10.47750/QAS/25.200.09