SOCIETY

Artificial intelligence in debtor-initiated bankruptcy proceedings

  • 1 Faculty of Social Science – Albanian University, Albania; Faculty of Law and Human Science - Mediterraneum University of Albania, Albania

Abstract

This paper examines the legal framework governing bankruptcy proceedings under Albanian legislation, focusing on the debtor’s status and procedural rights in initiating insolvency procedures. It distinguishes between current insolvency, where financial incapacity is evident, and prospective insolvency, where the debtor’s inability to meet obligations is reasonably foreseeable. The study analyzes the debtor’s petition for the commencement of bankruptcy proceedings, supported by the submission of financial documentation required to substantiate insolvency claims. Particular attention is given to the debtor’s request for reorganization and the submission of a reorganization plan as a mechanism for preserving economic value and improving creditor recovery through structured financial rehabilitation. In addition, the paper explores the integration of Artificial Intelligence (AI) as a decision-support tool in assessing insolvency risk and evaluating the feasibility of reorganization plans through predictive financial analysis. The findings highlight the role of bankruptcy law in ensuring legal certainty while promoting efficient and sustainable corporate restructuring processes.

Keywords

References

  1. Hoxha, T., Olldashi, E., Almahmodi, B. H., Annuk, A., Alkattan, H., & Abotaleb, M. Data-Driven Regression Modelling of Insolvency Outcomes: Judicial Efficiency, Foreign Participation, and Recovery Trends. International Journal of Innovative Technology and Interdisciplinary Sciences, 2026, 9(1), 172–209. https://doi.org/10.15157/ijitis.2026.9.1.172-209
  2. Blazy, R., Stef, N. Bankruptcy Procedures in Post-Transition Economies. Eur. J. Law Econ., 2020, 50, 7–64.
  3. Lan, S., Zhadigerova, O., Yermekova, Z., Syrlybayeva, N., Sigayev, Y. The Evolution of Corporate Shadow Banking Behavior Under Climate Risk: Insights from Resilience and Capital Structure. J. Risk Financial Manag. 2025,18, 701.
  4. Armour, J., Hsu, A., Walters, A. The Costs and Benefits of Secured Creditor Control in Bankruptcy: Evidence from the UK. Rev. Law Econ. 2006, 2(1), 101–135.
  5. Katz, D. M., Bommarito, M. J., Blackman, J. A General Approach for Predicting the Behavior of the Supreme Court of the United States. PLoS One 2017, 12 (4), e0174698.
  6. Jayawardana, J., Wijeratne, P., Vrcelj, Z., Sandanayake, M. Artificial Intelligence for Predicting Insolvency in the Construction Industry—A Systematic Review and Empirical Feature Derivation. Buildings 2025, 15, 2988. https://doi.org/10.3390/buildings15172988
  7. Hamdi, M., Mestiri, S., Arbi, A. Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models. J. Risk Financial Manag. 2024, 17, 132. https://doi.org/10.3390/jrfm17040132
  8. Gajdosikova, D., Michulek, J. Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees. Agriculture 2025, 15, 1077. https://doi.org/10.3390/agriculture15101077
  9. D’Ercole, A., Me, G. A Novel Approach to Company Bankruptcy Prediction Using Convolutional Neural Networks and Generative Adversarial Networks. Mach. Learn. Knowl. Extr. 2025, 7, 63. https://doi.org/10.3390/make7030063
  10. Kristóf, T., Virág, M. A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary. J. Risk Financial Manag. 2020, 13, 35. https://doi.org/10.3390/jrfm13020035
  11. Prusak, B. Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries. Int. J. Financial Stud. 2018, 6, 60. https://doi.org/10.3390/ijfs6030060
  12. Becerra-Vicario, R., Alaminos, D., Aranda, E., Fernández-Gámez, M.A. Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry. Sustainability 2020, 12, 5180. https://doi.org/10.3390/su12125180
  13. Balcaen, Sofie, and Hubert Ooghe. 2004. 35 Years of Studies on Business Failure: An Overview of the Classical Statistical Methodologies and Their Related Problems. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/248. Ghent: Ghent University, Faculty of Economics and Business Administration.
  14. Štefko, R., Horváthová, J., Mokrišová, M. The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy. J. Risk Financial Manag. 2021, 14, 220. https://doi.org/10.3390/jrfm14050220
  15. Bărbuță-Mișu, N., Madaleno, M. Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis. J. Risk Financial Manag. 2020, 13, 58. https://doi.org/10.3390/jrfm13030058
  16. Bărbuță-Mișu, N., Madaleno, M. Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis. J. Risk Financial Manag. 2020, 13, 58. https://doi.org/10.3390/jrfm13030058
  17. Bešlić Obradović, D.; Jakšić, D.; Bešlić Rupić, I.; Andrić, M. Insolvency prediction model of the company: The case of the Republic of Serbia. Econ. Res.-Ekon. Istraživanja 2018, 31, 139–157.
  18. Sousa Torres, J.A., da Silva, D.A., Albuquerque, R.d.O., Nze, G.D.A., Sandoval Orozco, A.L., García Villalba, L.J. Ontology Development for Asset Concealment Investigation: A Methodological Approach and Case Study in Asset Recovery. Appl. Sci. 2024, 14, 9654. https://doi.org/10.3390/app14219654
  19. Iotti, M.; Bonazzi, G. Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization ―OI Pomodoro da Industria Nord Italia‖. Sustainability 2018, 10, 947. https://doi.org/10.3390/su10040947
  20. Hoxha, T., Olldashi, E., AL-Thabhawee, G., Alkattan, H., & Abotaleb, M. The Role of the Court in the Bankruptcy Process: A Statistical and Comparative Case Study. International Journal of Innovative Technology and Interdisciplinary Sciences, 2025, 8(4), 1039–1081. https://doi.org/10.15157/ijitis.2025.8.4.1039-1081
  21. Krstic, K., Westerman, R., Chattu, V.K., V. Ekkert, N., Jakovljevic, M. Corona-Triggered Global Macroeconomic Crisis of the Early 2020s. Int. J. Environ. Res. Public Health 2020, 17, 9404. https://doi.org/10.3390/ijerph17249404
  22. Kitowski, J., Kowal-Pawul, A., Lichota, W. Identifying Symptoms of Bankruptcy Risk Based on Bankruptcy Prediction Models—A Case Study of Poland. Sustainability 2022, 14, 1416. https://doi.org/10.3390/su14031416
  23. Silva, A.F.d.; Brito, J.H.; Lourenço, M.; Pereira, J.M. Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence. Sustainability 2023, 15, 16482. https://doi.org/10.3390/su152316482

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