Predicting Company Bankruptcy Using Machine Learning

Bankruptcies cause massive financial losses not only for businesses but also for investors, lenders, and employees. Predicting bankruptcy early allows companies to take preventive action and helps investors and banks manage risks more effectively. However, bankruptcy is a rare event, making it a challenging problem for traditional statistical methods. Machine learning models can help uncover subtle signals of financial distress by analyzing patterns in financial statements, credit scores, and operational metrics.

Using financial datasets containing company balance sheets, profit-loss accounts, and operational indicators, machine learning classification models like Logistic Regression, Random Forests, XGBoost, and SVMs can predict bankruptcy risks. Imbalanced classification techniques such as SMOTE, weighted loss functions, or anomaly detection methods can be employed to deal with the rarity of bankruptcy cases, ensuring models are sensitive to early warning signs.

Financial data such as liquidity ratios, profitability ratios, leverage ratios, and operational efficiency metrics are collected for companies over time. After preprocessing and handling missing values, machine learning models are trained to classify businesses as solvent or at-risk of bankruptcy. Due to class imbalance, special techniques like oversampling or anomaly detection may be used. Model outputs are then visualized in risk dashboards to guide decision-making for investors, auditors, and business managers.

• Collect company financial datasets, including balance sheets, income statements, and financial ratios over multiple periods.
• Engineer features like current ratio, debt-to-equity ratio, net profit margin, ROA (Return on Assets), and operational cash flow metrics.
• Handle imbalanced classes using SMOTE, stratified sampling, or cost-sensitive classifiers during model training.
• Train classification models like Logistic Regression, Random Forest, SVM, or XGBoost to predict bankruptcy risks.
• Deploy dashboards displaying bankruptcy risk scores and company health indicators to assist stakeholders in preventive planning.

• Polish Companies Bankruptcy Dataset
• Imbalanced-Learn Library Documentation
• Financial Ratios for Bankruptcy Prediction