Early warning detection using Logic Learning Machine: Evidence from private firms

Journal title FINANCIAL REPORTING
Author/s Enrico Ferrari, Roberto Garelli, Alessandro Limon, Alessandro Piazza, Lorenzo Simoni, Damiano Verda
Publishing Year 2025 Issue 2025/1
Language English Pages 29 P. 21-49 File size 405 KB
DOI 10.3280/fr202516015
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Purpose: The paper aims to assess the ability of explainable artifiical intelligence (XAI), specifically Logic Learning Machine (LLM), to predict early signals of distress in private companies. Design/methodology/approach: We examined a sample of Italian private firms that activated early recovery procedures, which are matched to healthy firms. The proprietary algorithm developed by Rulex Innovation Labs is used to discriminate between distressed firms and healthy companies based on a set of publicly available data. Results are then compared with those obtained using other (widely employed) methods. Findings: The analysis shows that the LLM method is able to classify distressed firms with high accuracy, outperforming logit models and other AI-based methods. Originality/value: We contribute to the literature on the use of AI in insolvency prediction by exploring the predictive ability of XAI. We also extend the literature on insolvency in private firms, which represent a fundamental part of the economic system and are subject to less scrutiny than public firms. Practical implications: Our results have practical implications considering the recently enforced EU Insolvency Directive, which imposes the implementation of early warning tools that should be easy to use for all entities across all Member States. By using publicly available data on early distress procedures activated by companies, we build an early warning detection system that can be easily employed by companies of all sizes and types.

Keywords: insolvency, early warning, distress prediction, artificial intelligence, Logic Learning Machine, private firms

Jel codes: G33, M40

  1. Kalay, A., Singhal, R., & Tashjian, E. (2007). Is Chapter 11 costly?. Journal of Financial Economics, 84, 772-796.
  2. Ala-Pietilä, P., Bonnet, Y., Bergmann, U., Bielikova, M., Bonefeld-Dahl, C., Bauer, W., Bouarfa, L., Chatila, R., Coeckelbergh, M., Dignum, V., & Gagné, J.F. (2020). The assessment list for trustworthy artificial intelligence (ALTAI). European Commission.
  3. Altman, E. I., & Sabato, G. (2005). Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs. Journal of Financial Services Research, 28, 15-42.
  4. Altman, E. I., Kant, T., & Rattanaruengyot, T. (2009). Post-Chapter 11 Bankruptcy Performance: Avoiding Chapter 22. Journal of Applied Corporate Finance, 21(3), 53-64.
  5. Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model. Journal of International Financial Management & Accounting, 28(2), 131-171.
  6. Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
  7. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. DOI: 10.1145/3374549.3374550
  8. Bava, F., Di Trana, M. G., & Cane, M. (2020). Can a quantitative approach be mitigated? Proposals for the application of the “early warnings” required by the new Italian Insolvency Code. Financial reporting, 2, 33-61.
  9. Borchert, P., Coussement, K., De Caigny, A., & De Weerdt, J. (2023). Extending business failure prediction models with textual website content using deep learning. European Journal of Operational Research, 306(1), 348-357.
  10. Brasili, C., Bruno, F., & Saguatti, A. (2012). Economic growth and dualism in Italian regions: A spatiotemporal model. Rivista Italiana degli Economisti, 3, 397-416.
  11. Brockett, P. L., Cooper, W. W., Golden, L. L., & Pitaktong, U. (1994). A neural network method for obtaining an early warning of insurer insolvency. Journal of Risk and Insurance, 61, 402-402. DOI: 10.2307/253568
  12. Burgstahler, D. C., Hail, L., & Leuz, C. (2006). The importance of reporting incentives: Earnings management in European private and public firms. The Accounting Review, 81(5), 983-1016.
  13. Camacho-Miñano, M. D. M., Segovia-Vargas, M. J., & Pascual-Ezama, D. (2015). Which characteristics predict the survival of insolvent firms? An SME reorganization prediction model. Journal of Small Business Management, 53(2), 340-354.
  14. Charalambakis, E. C., & Garrett, I. (2019). On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece. Review of Quantitative Finance and Accounting, 52(2), 467-491.
  15. Chung, K. C., Tan, S. S., & Holdsworth, D. K. (2008). Insolvency prediction model using multivariate discriminant analysis and artificial neural network for the finance industry in New Zealand. International Journal of Business and Management, 39(1), 19-28.
  16. Consiglio Nazionale dei Dottori Commercialisti e degli Esperti Contabili (CNDCEC) (2019). Crisi d’Impresa. Gli Indici dell’Allerta. Consiglio Nazionale dei Dottori Commercialisti e degli Esperti Contabili (CNDCEC).
  17. Culotta, F., Alaimo, L. S., Bravo, J. M., di Bella, E., & Gandullia, L. (2022). Total-employed longevity gap, pension fairness and public finance: Evidence from one of the oldest regions in EU. Socio-Economic Planning Sciences, 82, Part A, 101221.
  18. Dainelli, F., Giunta, F., & Cipollini, F. (2013). Determinants of SME credit worthiness under Basel rules: the value of credit history information. PSL Quarterly Review, 66(264), 21-47.
  19. D’Annunzio, N., & Falavigna, G. (2004). Modelli di analisi e previsione del rischio di insolvenza: una prospettiva delle metodologie applicate. Ceris-Cnr.
  20. El Khoury, R., & Al Beaïno, R. (2014). Classifying manufacturing firms in Lebanon: An application of Altman’s model. Procedia-Social and Behavioral Sciences, 109(1), 11-18.
  21. European Union Directive 2019/1023 of the European Parliament and of the Council of 20 June 2019 on preventive restructuring frameworks, on discharge of debt and disqualifications, and on measures to increase the efficiency of procedures concerning restructuring, insolvency and discharge of debt (2019). -- https://eur-lex.europa.eu/eli/dir/2019/1023/oj.
  22. Ferrari, E., Verda, D., Pinna, N., & Muselli, M. (2023). Optimizing Water Distribution through Explainable AI and Rule-Based Control. Computers, 12(6), 123.
  23. Gerussi, A., Verda, D., Cappadona, C., Cristoferi, L. Bernasconi, D.P., Bottaro, S., Carbone, M. Muselli, M., Invernizzi, P., & Asselta, R. (2022). LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis. Journal of Personalized Medicine, 12, 1587.
  24. Giacosa, E., Mazzoleni, A., Teodori, C., & Veneziani, M. (2015). Insolvency prediction in companies: an empirical study in Italy. Corporate Ownership and Control, 12(4), 232-350.
  25. Guedhami, O., & Pittman, J. (2008). The importance of IRS monitoring to debt pricing in private firms. Journal of Financial Economics, 90(1), 38-58.
  26. Italian Government Legislative Decree 12 January 2019, n. 14 (2019). -- https://www.gazzettaufficiale.it/dettaglio/codici/codiceCrisi.
  27. Italian Government Legislative Decree 17 June 2022, n. 83 (2022). -- https://www.gazzettaufficiale.it/eli/id/2022/07/01/22G00090/sg.
  28. Jacoby, G., Li, J., & Liu, M. (2019). Financial distress, political affiliation and earnings management: the case of politically affiliated private firms. The European Journal of Finance, 25(6), 508-523. DOI: 10.1080/1351847X.2016.1233126
  29. Jones, S., & Wang, T. (2019). Predicting private company failure: A multi-class analysis. Journal of International Financial Markets, Institutions and Money, 61, 161-188.
  30. Kolay, M., Lemmon, M., & Tashjian, E. (2016). Spreading the misery? Sources of bankruptcy spillover in the supply chain. Journal of Financial and Quantitative Analysis, 51(6), 1955-1990. DOI: 10.1017/S0022109016000855
  31. Lagravinese, R. (2015). Economic crisis and rising gaps North-South: evidence from the Italian regions. Cambridge Journal of Regions, Economy and Society, 8(2), 331-342.
  32. Mafrolla, E., & D’Amico, E. (2017). Borrowing capacity and earnings management: An analysis of private loans in private firms. Journal of Accounting and Public Policy, 36(4), 284-301.
  33. Matenda, F. R., Sibanda, M., Chikodza, E., & Gumbo, V. (2022). Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research. Management Review Quarterly, 72, DOI: 10.1007/s11301-021-00216-x
  34. Moscatelli, M., Narizzano, S., Parlapiano, F., & Viggiano, G. (2019). Corporate default forecasting with machine learning. Banca d’Italia working papers, n. 1256.
  35. Muselli, M. (2006). Switching Neural Networks: A New Connectionist Model for Classification. Neural Nets – WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931, Springer.
  36. Muselli, M. (2012). Extracting knowledge from biomedical data through Logic Learning Machines and Rulex. EMBnet.journal, 18, 56-58.
  37. Muselli M., & Ferrari E. (2011). Coupling logical analysis of data and shadow clustering for partially defined positive Boolean function reconstruction. IEEE Transaction on Knowledge and Data Engineering, 23, 37-50. DOI: 10.1109/TKDE.2009.206
  38. Orlando, T., & Rodano, G. (2020). Firm undercapitalization in Italy: business crisis and survival before and after COVID-19. Banca d’Italia occasional papers, n. 590.
  39. Papana, A., & Spyridou, A. (2020). Bankruptcy prediction: the case of the Greek market. Forecasting, 2(4), 505-525.
  40. Parodi, S., Filiberti, R., Marroni, P., Libener, R., Ivaldi, G.P., Mussap, M., Ferrari, E., Manneschi, C., Montani, E., & Muselli, M. (2015). Differential diagnosis of pleural mesothelioma using Logic Learning Machine. BMC bioinformatics, 16, 1-10. DOI: 10.1186/1471-2105-16-S9-S3.
  41. Peek, E., Cuijpers, R., & Buijink, W. (2010). Creditors’ and shareholders’ reporting demands in public versus private firms: Evidence from Europe. Contemporary Accounting Research, 27(1), 49-91.
  42. Radovanovic, J., & Haas, C. (2023). The evaluation of bankruptcy prediction models based on socio-economic costs. Expert Systems with Applications, 227, 120275.
  43. Range, M. M., Njeru, A., & Waititu, G. A. (2018). Using Altman’s Z score (Sales/Total Assets) Ratio Model in Assessing Likelihood of Bankruptcy for Sugar Companies in Kenya. International Journal of Academic Research in Business and Social Sciences, 8(6), 683-703.
  44. Scherger, V., Terceño, A., & Vigier, H. (2019). A systematic overview of the prediction of business failure. International Journal of Technology, Policy and Management, 19(2), 196-211. DOI: 10.1504/IJTPM.2019.100601
  45. Šlefendorfas, G. (2016). Bankruptcy prediction model for private limited companies of Lithuania. Ekonomika, 95(1), 134-152.
  46. Tsai, C. F., & Wu, J. W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639-2649.
  47. Vaertto, F. (1998). Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking & Finance, 22, 1421-1439. DOI: 10.1016/S0378-4266(98)00059-4
  48. Varetto, F. (1999). Metodi di previsione delle insolvenze: un’analisi comparata. In G. Szego & F. Varetto (Eds.), Il Rischio Creditizio: Misura e Controllo (pp. 178-301). Torino: Utet.
  49. Verda, D., Parodi, S., Ferrari, E., & Muselli, M. (2019). Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods. BMC bioinformatics, 20(9), 1-13.

Enrico Ferrari, Roberto Garelli, Alessandro Limon, Alessandro Piazza, Lorenzo Simoni, Damiano Verda, Early warning detection using Logic Learning Machine: Evidence from private firms in "FINANCIAL REPORTING" 1/2025, pp 21-49, DOI: 10.3280/fr202516015