How AI Is Learning to Predict Which Companies Will Fail

Corporate failures often appear sudden from the outside, yet most troubled firms show signs of decline long before they collapse. The challenge is that these signals can be subtle, inconsistent, or buried within large volumes of financial data. In a new study, Prof. Nagaraju Thota, Prof. A. C. V. Subrahmanyam, Sreenivasulu Puli, and Sneha Yarala from the Dept. of Economics & Finance- BITS Pilani, Hyderabad Campus explore how artificial intelligence and machine learning can uncover these patterns more accurately than traditional methods. Their research examines the financial behaviour of thousands of Indian firms and offers insights into what makes bankruptcy predictable and why public listed companies are far easier to assess than unlisted ones. 

Why Bankruptcy Prediction Is So Difficult

Predicting bankruptcy has always been a complex problem because corporate financial statements vary widely in format and quality. Companies differ in leverage, investment cycles, growth strategies, and accounting practices. Traditional statistical models often assume that the data is balanced and evenly distributed, but in reality there are very few bankrupt firms compared to a vast number of healthy firms.

This imbalance is a major issue as the authors point out that out of 18,771 total firms, only 1,492 were bankrupt, while 17,279 were non bankrupt. This means that most prediction models are trained on data dominated by healthy firms, making it difficult to identify the minority group accurately. Bankruptcy is therefore a rare event, and without corrective methods the machine learning model tends to learn patterns only from the majority class, resulting in poor predictive performance.

How AI Models Improve the Prediction Landscape

To overcome this imbalance, the researchers employed the Synthetic Minority Oversampling Technique, known as SMOTE. This method creates synthetic data points for the underrepresented class and expands the dataset to 34,558 balanced observations, allowing the models to learn the characteristics of financially distressed firms more effectively. 

The study then tested a range of machine learning techniques including random forests, gradient boosting, neural networks, decision trees, and logistic regression. These models do not rely on strict statistical assumptions. Instead they detect complex relationships across financial ratios and can capture early signs of vulnerability that traditional tools might miss.

Why Listed Companies Are Much Easier to Predict

One of the most important findings of the study is the clear distinction between listed and unlisted companies. Listed firms must follow regulatory reporting standards and provide regular financial disclosures and their accounting data is timelier and more structured, which leaves fewer gaps for the models to interpret. As a result, the prediction accuracy for listed companies is significantly higher.

Unlisted companies, on the other hand, have more inconsistent financial disclosures. Limited reporting and irregular information mean the risk signals are harder to detect. The presence of fewer data points makes their behaviour less predictable, even when sophisticated machine learning tools are applied. The study highlights that data transparency is essential for reliable bankruptcy prediction and that public listing indirectly supports better risk assessment.

The Financial Red Flags That Matter Most

Although machine learning models can evaluate hundreds of variables at once, the study also identifies the financial indicators that consistently have the greatest predictive power. These include:

• Debt to total assets

• Return on assets

• Interest coverage

• Profit after taxes to total assets

• Cash flow to debt

These ratios are deeply tied to a company’s ability to manage its obligations, generate returns from its assets, and maintain healthy cash flows. When these values weaken together they create a powerful warning signal and Machine learning models excel at recognising this combination and translating it into an accurate risk prediction. 

Which Models Perform Best

Among all the models tested, random forests delivered the strongest performance, achieving an accuracy of 0.93 in the overall sample. Gradient boosting and neural networks also performed well, showing strong adaptability to the complex financial patterns present in the data. Logistic regression remained useful as a benchmark but did not match the predictive strength of the more advanced machine learning techniques. These results demonstrate that ensemble and deep learning approaches are particularly effective when financial data is varied, unbalanced, and complex.

Why This Research Matters for India’s Financial System

India’s business landscape is rapidly expanding, and with this growth comes the need for better risk monitoring tools. Early prediction of bankruptcy can help banks manage credit exposure, help investors identify vulnerable companies, and guide policymakers in strengthening governance standards.

For companies themselves, these models can serve as internal health checks. They allow managers to understand which financial areas deserve urgent attention before they become irreversible problems. Most importantly, the study provides evidence that transparency and consistency in financial reporting lead to better predictive outcomes. Listed companies benefit from this, and the findings underline the importance of strong disclosure norms across all sectors.

Looking Ahead

The study shows how data driven methods can bring clarity to a long standing challenge in corporate finance. Bankruptcy is not just an individual firm’s failure. It affects employees, creditors, suppliers, and the larger economic network. AI based tools have the potential to act as early warning systems that support more stable and responsible financial ecosystems.

As more Indian companies embrace digital reporting and standardised disclosures, machine learning models will become even more accurate. The future of financial risk management may increasingly depend on intelligent systems that learn from real economic behaviour and guide decisions with precision and foresight.