Purpose: The primary goal was to forecast insolvency by comparing different bankruptcy prediction methods among Indian industrial enterprises.

Methodology: This study assessed the use of machine learning models in the financial industry. A comparison of random forests (RFs), decision trees (DTs), artificial neural networks (ANNs), and logistic regression was done. The consideration period for companies that were declared bankrupt under the 2016 Insolvency and Bankruptcy Code was April 1, 2017, until March 31, 2020. Data from 48 companies, 24 of which were bankrupt and 24 of which were not, was gathered two years ago.

Findings: In the comparative examination, the RF predictive technique outperformed the other predictive strategies in terms of accuracy.Practical

Implications: A company’s financial characteristics provide valuable insights into its overall financial well-being. Examining a field of information that would be fascinating to regulators and investors will be facilitated by this study.

Originality: There has been little research on bankrupt companies after the IBC 2016 took effect. This study concentrated on predicting a company’s bankruptcy following the implementation of the 2016 Insolvency and Bankruptcy Code.

1. Agrawal, K., & Maheshwari, Y. (2019). Efficacy of industry factors for corporate default prediction. IIMB Management Review, 31(1), 71–77. https://doi.org/10.1016/j.iimb.2018.08.007
2. Ahuja, B. R., & Singhal, N. (2014). Assessing the financial soundness of companies with special reference to the Indian textile sector: An application of the Altman Z score model. Indian Journal of Finance, 8(4), 38–48. https://doi.org/10.17010/ijf/2014/v8i4/71922
3. Alifiah, M. N. (2014). Prediction of financial distress companies in the trading and services sector in Malaysia using macroeconomic variables. Procedia - Social and Behavioral Sciences, 129, 90–98. https://doi.org/10.1016/j.sbspro.2014.03.652
4. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933
5. Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (The Italian experience). Journal of Banking & Finance, 18(3), 505–529. https://doi.org/10.1016/0378-4266(94)90007-8
6. Argenti, J. (1976). Corporate collapse: The causes and symptoms. McGraw-Hill.
7. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006
8. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
9. Bhunia, A., & Sarkar, R. (2011). A study of financial distress based on MDA. Journal of Management Research, 3(2), 1–11. https://doi.org/10.5296/jmr.v3i2.549
10. Binti, S., Zeni, M., & Ameer, R. (2010). Turnaround prediction of distressed companies: Evidence from Malaysia. Journal of Financial Reporting and Accounting, 8(2), 143–159. https://doi.org/10.1108/19852511011088398
11. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
12. Cao, Y., Wan, G., & Wang, F. (2011). Predicting financial distress of Chinese listed companies using rough set theory and support vector machine. Asia-Pacific Journal of Operational Research, 28(1), 95–109. https://doi.org/10.1142/S0217595911003077
13. Chandra, D. K., Ravi, V., & Bose, I. (2009). Failure prediction of dotcom companies using hybrid intelligent techniques. Expert Systems with Applications, 36(3), 4830–4837. https://doi.org/10.1016/j.eswa.2008.05.047
14. Chandra, S., & Awasthi, R. (2019). Insolvency risk: Issues and challenges for public sector commercial banks of India. Indian Journal of Finance, 13(12), 19–33. https://doi.org/10.17010/ijf/2019/v13i12/149266
15. Chatterjee, A. (2018). Predicting corporate financial distress for widely held large - cap companies in India : Altman model vs. Ohlson model. Indian Journal of Finance, 12(8), 36–49. https://doi.org/10.17010/ijf/2018/v12i8/130743
16. Chen, J., Marshall, B. R., Zhang, J., & Ganesh, S. (2006). Financial distress prediction in China. Review of Pacific Basin Financial Markets and Policies, 9(2), 317–336. https://doi.org/10.1142/S0219091506000744
17. Chen, M.-Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272. https://doi.org/10.1016/j.eswa.2011.02.173
18. Chi, D.-J., & Shen, Z.-De. (2022). Using hybrid artificial intelligence and machine learning technologies for sustainability in going-concern prediction. Sustainability, 14(3), 1810. https://doi.org/10.3390/su14031810
19. Chitta, S., Jain, R. K., & Sriharsha, R. (2019). Financial soundness of Maharatna companies: Application of Altman Z score model. Indian Journal of Finance, 13(10), 22–33. https://doi.org/10.17010/ijf/2019/v13i10/147745
20. Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 142–155. https://www.jstor.org/stable/3665934
21. Creamer, G. G., & Freund, Y. (2004). Predicting performance and quantifying corporate governance risk for Latin American Adrs and banks. Available at SSRN. https://ssrn.com/abstract=743209
22. Fallahpour, S., Lakvan, E. N., & Zadeh, M. H. (2017). Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem. Journal of Retailing and Consumer Services, 34, 159–167. https://doi.org/10.1016/j.jretconser.2016.10.002
23. Farooq, U., Jibran Qamar, M. A., & Haque, A. (2018). A three-stage dynamic model of financial distress. Managerial Finance, 44(9), 1101–1116. https://doi.org/10.1108/MF-07-2017-0244
24. Gupta, N., & Gupta, V. (2023). The saga of Ruchi Soya Industries Limited: Could credit risk models predict bankruptcy? Indian Journal of Finance, 17(3), 64–76. https://doi.org/10.17010/ijf/2023/v17i3/172673
25. Hafeez, A., & Kar, S. (2021). Looking beyond the financial numbers: The relationship between macroeconomic indicators and the likelihood of financial distress. Global Business Review, 22(3), 674–688. https://doi.org/10.1177/0972150918811716
26. Halteh, K., Kumar, K., & Gepp, A. (2018). Financial distress prediction of Islamic banks using tree-based stochastic techniques. Managerial Finance, 44(6), 759–773. https://doi.org/10.1108/MF-12-2016-0372
27. Hertz, J., Krogh, A., Palmer, R. G., & Horner, H. (1991). Introduction to the theory of neural computation. Physics Today, 44(12), 70. https://doi.org/10.1063/1.2810360
28. Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems With Applications, 117, 287–299. https://doi.org/10.1016/j.eswa.2018.09.039
29. Hu, Y.-C., & Ansell, J. (2007). Measuring retail company performance using credit scoring techniques. European Journal of Operational Research, 183(3), 1595–1606. https://doi.org/10.1016/j.ejor.2006.09.101
30. Insolvency and Bankruptcy Board of India. (2022). Frequently asked questions on the Insolvency and Bankruptcy Code, 2016 (Revised January 2022 Edition). The Institute of Chartered Accountants of India. https://ibbi.gov.in/uploads/publication/6adaf64e3d3221399cfcda795de38a23.pdf
31. Jain, N., & Bothra, N. (2016). Financial performance of the luxury market: A study of pre and post financial crisis 2007-08. Indian Journal of Finance, 10(1), 28–40. https://doi.org/10.17010/ijf/2016/v10i1/85841
32. Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., & Franco, L. (2010). Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine, 50(2), 105–115. https://doi.org/10.1016/j.artmed.2010.05.002
33. John, N. (2020, June 23). Bankruptcy doubles to 3,774 in FY20; manufacturing, construction worst-hit. Business Today. https://www.businesstoday.in/industry/banks/story/bankruptcy-applications-double-to-3774-in-fy20-manufacturing-construction-worst-hit-sectors-261840-2020-06-22
34. Kim, H., Cho, H., & Ryu, D. (2020). Corporate default predictions using machine learning: Literature review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325
35. Kim, S. Y. (2018). Predicting hospitality financial distress with ensemble models: The case of US hotels, restaurants, and amusement and recreation. Service Business, 12(3), 483–503. https://doi.org/10.1007/s11628-018-0365-x
36. Lakshan, A. M., & Wijekoon, W. M. (2012). Predicting corporate failure of listed companies in Sri Lanka. GSTF Business Review (GBR), 2(1), 180. http://repository.kln.ac.lk/handle/123456789/11735
37. Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094–15102. https://doi.org/10.1016/j.eswa.2011.05.035
38. Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., Venkatesh, S., & Berk, M. (2016). Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view. Journal of Medical Internet Research, 18(12), e232. https://doi.org/10.2196/jmir.5870
39. Mahapatra, U., Nayak, S. M., & Rout, M. (2020). A systematic approach to enhance the forecasting of bankruptcy data. In, H. Das, P. Pattnaik, S. Rautaray, & K. C. Li (eds.), Progress in computing, analytics and networking. Advances in intelligent systems and computing (Vol. 1119). (pp. 641–650). Springer. https://doi.org/10.1007/978-981-15-2414-1_64
40. Manral, V. (2022, November 14). How is India emerging in the global manufacturing sector. Times of India. https://timesofindia.indiatimes.com/blogs/voices/how-is-india-emerging-in-the-global-manufacturing-sector/
41. Mondal, A., & Roy, D. (2013). Financial indicators of corporate sickness: A study of Indian steel industry. South Asian Journal of Management, 20(2), 85–101. https://www.proquest.com/openview/56663c1d5e63f8ee348ef4beb527a5fc/1?pq-origsite=gscholar&cbl=46967
42. Mselmi, N., Lahiani, A., & Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis, 50, 67–80. https://doi.org/10.1016/j.irfa.2017.02.004
43. Nandi, A., Sengupta, P. P., & Dutta, A. (2019). Diagnosing the financial distress in oil drilling and exploration sector of India through discriminant analysis. Vision, 23(4), 364–373. https://doi.org/10.1177/0972262919862920
44. Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. 1990 IJCNN International Joint Conference on Neural Networks, 2, 163–168. IEEE. https://doi.org/10.1109/ijcnn.1990.137710
45. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
46. Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464–473. https://doi.org/10.1016/j.dss.2011.10.007
47. Ong, S.-W., Choong Yap, V., & Khong, R. W. (2011). Corporate failure prediction: A study of public listed companies in Malaysia. Managerial Finance, 37(6), 553–564. https://doi.org/10.1108/03074351111134745
48. PwC. (2016). Sick Industrial Companies (Special Provisions) Act, 1985 repealed and BIFR/AIFR dissolved. https://www.pwc.in/assets/pdfs/news-alert-tax/2016/pwc_news_alert_1_december_2016_sick_industrial_companies_act_1985_repealed_and_bifr-aifr_dissolved.pdf
49. Ramesh, A., & Senthil Kumar, C. B. (2018). Asset and debt management ratios in bankruptcy prediction - Evidence from India. Indian Journal of Finance, 12(8), 50–63. https://doi.org/10.17010/ijf/2018/v12i8/130744
50. Saji, T. G. (2018). Financial distress and stock market failures: Lessons from Indian realty sector. Vision, 22(1), 50–60. https://doi.org/10.1177/0972262917750244
51. Shilpa Shetty, H., & Vincent, T. N. (2021). Corporate default prediction model: Evidence from the Indian industrial sector. Vision. https://doi.org/10.1177/09722629211036207
52. Singh, B. P., & Mishra, A. K. (2016). Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies. Financial Innovation, 2(1), 6. https://doi.org/10.1186/s40854-016-0026-9
53. Smith, M., & Alvarez, F. (2022). Predicting firm-level bankruptcy in the Spanish economy using extreme gradient boosting. Computational Economics, 59(1), 263–295. https://doi.org/10.1007/s10614-020-10078-2
54. Springate, G. L. (1978). Predicting the possibility of failure in a Canadian firm: A discriminant analysis. Simon Fraser University.
55. Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89–100. https://doi.org/10.1016/j.jbankfin.2014.12.003
56. Viswanatha Reddy, C. (2012). Analysis of liquidity, profitability, risk and financial distress: A case study of Dr. Reddy's Laboratories Ltd. Indian Journal of Finance, 6(12), 5–17. https://www.indianjournaloffinance.co.in/index.php/IJF/article/view/72358
57. What is Bankruptcy? Definition of bankruptcy, bankruptcy meaning. (2022). The Economic Times. https://economictimes.indiatimes.com/definition/bankruptcy
58. Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowledge-Based Systems, 26, 196–206. https://doi.org/10.1016/j.knosys.2011.08.001
59. Yu, Q., Miche, Y., Séverin, E., & Lendasse, A. (2014). Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, 296–302. https://doi.org/10.1016/j.neucom.2013.01.063
60. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859