Conference Papers

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    Bankruptcy Prediction Using Bi-Level Classification Technique
    (Springer Science and Business Media Deutschland GmbH, 2023) Antani, A.; Annappa, B.; Dodia, S.; Manoj Kumar, M.V.
    Bankruptcy is a legal proceeding involving a person or a business, where they are unable to pay the debt. Financial investors, banks, money lenders, and the government seek to know the status of bankruptcy of firms as it carries huge financial risk. The prediction of bankruptcy will help all the stakeholders of the company. To model bankruptcy prediction, traditional statistical methods like multiple discriminant analysis and Machine Learning (ML) models like Decision Trees, Support Vector Machines, and Ensemble have been utilized. In existing works, homogeneous base estimators are used while developing ensemble algorithms. This study uses a bi-level classification technique (a heterogeneous ensemble ML technique) to predict bankruptcy. To train the classifier, the features extracted are Altman z-score parameters and market-based measures. Unlike previous studies, this study uses an indicator of corporate governance as a feature. The outcome of this study is an improvement in the performance of the ML model using the bi-level classification technique. An F1-score of 0.98 and 97.8% accuracy is achieved with features including Tobin’s Q and bi-level classification technique as an ML model. It outperforms the 96% accuracy of the random forest algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Machine Learning Solutions for Predicting Bankruptcy in Indian Firms
    (Springer Science and Business Media Deutschland GmbH, 2025) Chaithra; Sharma, P.; Mohan, R.
    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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.