Abstract
The growing demand to identify potential bankrupt companies has prompted more research into bankruptcy prediction, assisting stakeholders in determining the worthiness of an investment. The Indian stock market offers investment opportunities, but it also involves risk. As a result, it is critical to invest in fundamentally sound companies for long-term investment. To address this need, we created a machine learning-based model for identifying a healthy and distressed firm in the Indian scenario. We created a dataset consisting of 118 bankrupt and 310 healthy firms. The dataset contains three labels: bankrupt, healthy, and financial distress. The addition of the financial distress category improves our ability to recognize and identify firms that are more likely to declare bankruptcy. Recognizing the shortcomings of limited data in the Indian scenario in previous research, our study aimed to include more data instances for training. The dataset included widely recognized financial ratios and macroeconomic data that recognize the interconnectedness of broader economic trends with the company’s financial health. Advanced machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Categorical Boosting (CatBoost), Gradient Boost (GB), and K-Nearest Neighbors (KNN) were applied. The XGBoost and LGBM demonstrated the highest level of classification accuracy and also performed well on real-world data, demonstrating their potential use in supporting investors with decision-making processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Abdullah, M., et al.: Dynamics of speed of leverage adjustment and financial distress in the Indian steel industry. J. Open Innov. Technol. Market Complex. 9(4), 100152 (2023)
Arora, P., Saurabh, S.: Predicting distress: a post insolvency and bankruptcy code 2016 analysis. J. Econ. Finance 46(3), 604–622 (2022)
Bapat, V., Nagale, A.: Comparison of bankruptcy prediction models: evidence from India. Acc. Finance Res. 3(4), 91–98 (2014)
Barboza, F., Kimura, H., Altman, E.: Machine learning models and bankruptcy prediction. Expert Syst. Appl. 83, 405–417 (2017)
Ghosh, A., Kapil, S.: Is Altman’s model efficient in predicting bankruptcy? - A comparison among the Altman z-score, dea, and ann models. J. Inf. Optim. Sci. 43(6), 1191–1207 (2022)
Kanapickienė, R., Kanapickas, T., Nečiūnas, A.: Bankruptcy prediction for micro and small enterprises using financial, non-financial, business sector and macroeconomic variables: the case of the lithuanian construction sector. Risks 11(5), 97 (2023)
Kanojia, S., Gupta, S.: Bankruptcy in Indian context: perspectives from corporate governance. J. Manag. Gov. 27(2), 505–545 (2023)
Keswani, S., Wadhwa, B.: Withdrawn: association among the selected macroeconomic factors and Indian stock returns (2021)
Mai, F., Tian, S., Lee, C., Ma, L.: Deep learning models for bankruptcy prediction using textual disclosures. Eur. J. Oper. Res. 274(2), 743–758 (2019)
Mancisidor, R.A., Aas, K.: Using multimodal learning and deep generative models for corporate bankruptcy prediction. arXiv preprint arXiv:2211.08405 (2022)
Montesinos, A.: Profit prediction based on financial statements using deep neural network. In: 2022 IEEE World AI IoT Congress (AIIoT), pp. 533–537. IEEE (2022)
Oberoi, S.S., Banerjee, S.: Bankruptcy prediction of Indian banks using advanced analytics. Econ. Stud. J. 4, 22–41 (2023)
Özparlak, G., Dilidüzgün, M.Ö.: Corporate bankruptcy prediction using machine learning methods: the case of the USA. Uluslararası Yönetim İktisat ve İşletme Dergisi 18(4), 1007–1031 (2022)
Pisula, T.: An ensemble classifier-based scoring model for predicting bankruptcy of polish companies in the podkarpackie voivodeship. J. Risk Financ. Manag. 13(2), 37 (2020)
Rasolomanana, O.M.: Bankruptcy prediction model using machine learning. Ph.D. thesis (2022)
Saladi, S.D., Yarlagadda, R.: An enhanced bankruptcy prediction model using fuzzy clustering model and random forest algorithm. Revue d’Intelligence Artificielle 35(1) (2021)
Senbet, L.W., Wang, T.Y., et al.: Corporate financial distress and bankruptcy: a survey. Found. Trends® Finance 5(4), 243–335 (2012)
Shetty, S., Musa, M., Brédart, X.: Bankruptcy prediction using machine learning techniques. J. Risk Financ. Manag. 15(1), 35 (2022)
Shetty, S.H., Vincent, T.N.: The role of board independence and ownership structure in improving the efficacy of corporate financial distress prediction model: evidence from India. J. Risk Financ. Manag. 14(7), 333 (2021)
Shrivastav, S.K., Ramudu, P.J.: Bankruptcy prediction and stress quantification using support vector machine: evidence from Indian banks. Risks 8(2), 52 (2020)
Singh, B.P., Mishra, A.K.: Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies. Financ. Innov. 2, 1–28 (2016)
Soui, M., Smiti, S., Mkaouer, M.W., Ejbali, R.: Bankruptcy prediction using stacked auto-encoders. Appl. Artif. Intell. 34(1), 80–100 (2020)
Volkov, A., Benoit, D.F., Van den Poel, D.: Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination. Decis. Support Syst. 98, 59–68 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chaithra, Sharma, P., Mohan, B.R. (2025). Machine Learning Solutions for Predicting Bankruptcy in Indian Firms. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15303. Springer, Cham. https://doi.org/10.1007/978-3-031-78122-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-78122-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-78121-6
Online ISBN: 978-3-031-78122-3
eBook Packages: Computer ScienceComputer Science (R0)
