An Explainable Hybrid Artificial Intelligence Model for Corporate Financial Distress Prediction Using Accounting Information

Naser Mastchaman; Elnaz  Akbarlou

Authors

Naser Mastchaman Department of Accounting, He.C., Islamic Azad University, Heris, Iran Author https://orcid.org/0000-0001-8368-7634
Elnaz Akbarlou Department of Management, Economics and Accounting, Payame Noor University, Tehran, Iran Corresponding Author https://orcid.org/0000-0001-6686-7924

Keywords:

accounting information; artificial intelligence; bankruptcy prediction; explainable AI; financial distress; machine learning; SHAP; stacking ensemble

Abstract

OThis study aimed to develop an explainable hybrid artificial-intelligence model for predicting corporate financial distress using accounting information and to compare its predictive performance with conventional bankruptcy models and individual machine-learning algorithms. A longitudinal firm-year dataset comprising 2,946 observations from 285 non-financial companies listed on the Tehran Stock Exchange and Iran Fara Bourse was constructed. Financial distress was defined as first entry into the Article 141 condition in the subsequent year. Logistic regression, support vector machine, random forest, XGBoost, and artificial neural network models were developed and subsequently integrated through stacked generalization. Feature selection, class-imbalance management, time-aware validation, and SHapley Additive exPlanations (SHAP) were incorporated. The most recent 750 firm-year observations were retained as an untouched out-of-time test sample. Of the total observations, 247 (8.4%) subsequently became financially distressed. Machine-learning models outperformed traditional bankruptcy benchmarks, with XGBoost achieving the strongest performance among individual classifiers (ROC-AUC = .921; PR-AUC = .612). The hybrid stacking model produced the best overall performance (ROC-AUC = .938; PR-AUC = .684; sensitivity = .823; specificity = .927; balanced accuracy = .875). SHAP analysis identified accumulated losses, operating cash flow, leverage, profitability, working capital, retained earnings, and interest coverage as the principal predictors. After removing accumulated losses to registered capital, the model retained strong predictive ability (ROC-AUC = .919). Combining accounting information with stacked artificial-intelligence models provides more accurate, robust, and interpretable financial-distress forecasts than conventional statistical models or individual machine-learning algorithms.

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