Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1642-1647· 0 citations· 22 references
Abstract
Timely bankruptcy of the corporations is a major issue that investors, financial institutions, and regulatory bodies need to know in order to reduce economic losses and enhance decisions on risk management. Nevertheless, bankruptcy forecasting is difficult because of extreme imbalance in classes and nonlinear correlation between financial data. This paper suggests a machine learning model of corporate bankruptcy prediction, which is explainable and statistically justified through advanced ensemble learning methods. A comparative study was conducted on a financial dataset based on Logistic Regression, Random Forest, XGBoost, LightGBM, Tuned LightGBM, and Stacking Ensemble models with $\mathbf{6, 8 1 9}$ firms and $\mathbf{9 5}$ attributes. In order to solve the problem of data imbalance, threshold optimization was used, and the optimal decision threshold was obtained (0.13). Accuracy, Precision, Recall, F1-score, ROC-AUC, PR- AUC, Matthews Correlation Coefficient, and Brier Score were used to measure model performance. The optimized LightGBM model had a better performance with the following parameters: F1-score of 0.5124, MCC of 0.4999, ROC-AUC of 0.9549 and a Brier Score of 0.0234, which showed high discrimination and good probability calibration. The explainability of the proposed framework with the help of SHAP and the statistical test developed by McNemar additionally confirmed the strength and interpretability of the proposed framework, which is why it can be applied to real-world financial risk assessment.
Bankruptcy is one of the biggest threats to a company's reputation, occurring when it is unable to pay back outstanding debts to banks, lenders, and suppliers. Predicting bankruptcy accurately and promptly allows companies to take remedial action in advance and avoid it. To achieve this goal, current research is investigating a combination of techniques that can accurately predict bankruptcy. The proposed method employs various ensemble techniques to combine the best methods for improved accuracy. The proposed ensemble models have been compared using both the original imbalanced dataset and the balanced dataset created by oversampling. The proposed method of using the balanced dataset on the ensemble models outperformed the models using the original dataset in accuracy and other performance metrics. Balance Bagging achieved the highest accuracy at 98.77%, followed by Random Forest at 98.68%, and AdaBoost at 96.9%. These results are a significant achievement compared to state-of-the-art techniques.
S. O. Olatunji, Dhiaa Musleh, Atta-ur Rahman et al.· Journal of Intelligent Decis...· 0 citations
This study aims to enhance bankruptcy prediction models by employing the gradient‐boosting TreeNet algorithm. This research assesses the predictive accuracy of TreeNet in bankruptcy classification using a high‐dimensional approach. Specifically, it categorizes bankruptcy as nonbankrupt, Chapter 7, or Chapter 11 categories. This research utilizes a large dataset comprised of 76,069 firm‐year observations for the years between 1991 and 2019. The findings highlight the high classification accuracy of TreeNet, particularly in predicting Chapter 7 cases, and its robust performance across different time horizons. The TreeNet prediction model is a valuable tool for decision‐making and risk management in finance, auditing, and policymaking sectors.
A. Saeedi· Intelligent Systems in Accou...· 0 citations
Credit risk in business-to-business (B2B) transactions can threaten financial stability if it is not managed effectively. This study evaluates the use of machine learning (ML) methods to predict credit risk in B2B transactions, using 4,828 observations from large companies in Bosnia and Herzegovina over a five-year period. Three ensemble-based ML models (Bagging Decision Tree, Random Forest, and Gradient Boosting) were compared with logistic regression. All ML models showed strong predictive performance, with Gradient Boosting performing slightly better overall. Liquidity, activity, and leverage indicators were the most important predictors across all models, while non-financial variables made only a limited contribution. The findings highlight the importance of accounting information in credit risk assessment and are relevant to IFRS 9, where probability of default is a key input to expected credit loss estimation under the general approach. These results provide a basis for further development of credit risk models for non-financial companies.
Suzi Mikulić· Ekonomska Misao i Praksa· 0 citations
The study focused on developing a creditworthiness prediction model utilizing artificial neural network. Credit risk evaluation has a relevant role for financial institutions, as lending could result in real and immediate losses. In particular, default prediction was one of the most challenging activities in managing credit risk. The objective was to enhance the accuracy and reliability of credit risk assessments by leveraging the computational power and learning capabilities of artificial neural networks. The parameters of the dataset include the customer's place of work, loan history, monthly salary, loan amount, transaction history, and credit history, all stored in the trained database. When a customer comes to apply for a loan, the system checks if the user is qualified based on these parameters and then approve or disapprove the loan accordingly. Rigorous testing and validation were conducted to ensure the model's robustness and generalizability. The results demonstrated that the neural network-based model significantly outperformed traditional statistical methods, providing more precise predictions of creditworthiness. An Object-Oriented Analysis and Design Methodology (OOADM) approach, which incorporated Unified Modeling Language for analysis and design, was used. The development stage was completed using a set of software tools, including Python and the MySQL database system.
E. C., Uzo Blessing Chimezie, Ukekwe Emmanuel C· International Journal of Lat...· 0 citations
The evaluation of accounting transactions is increasingly challenging due to the growing volume of financial records, severe class imbalance, and the limited transparency of existing audit support systems. Many current machine learning approaches emphasize prediction accuracy while providing insufficient interpretability and weak support for risk-oriented audit decisions. To address these issues, this paper proposes an Intelligent Accounting framework based on explainable machine learning for risk-oriented transaction outcome prediction. The proposed framework integrates accounting-driven feature engineering, supervised learning, SHAP based explainable artificial intelligence, and probability-based risk scoring into a unified decision-support pipeline. Logistic Regression is adopted as the core predictive model due to its robustness, interpretability, and model parsimony under highly imbalanced transaction data. Experimental results on accounting dataset consisting of 1,000 transaction records show that Logistic Regression achieved the highest PR-AUC of 0.9737 and ROC-AUC of 0.6458 compared with Random Forest and XGBoost. The risk scoring mechanism also ranked problematic transactions within the highest-risk group, supporting audit prioritization. In addition, graphical SHAP analysis provides qualitative insights by identifying Operating Expenses, log_Operating Expenses, transaction timing, Transaction Volume, Profit Margin, Revenue, Expenditure, Cash Flow, Gross Profit, and Accuracy Score as influential factors affecting transaction outcomes. These findings show that the proposed framework not only predicts transaction outcomes but also explains the accounting factors behind each decision. Overall, this study transforms conventional transaction classification into an interpretable, risk-oriented, and audit-driven intelligent accounting system for transparent financial decision support.
J. K. Siregar, Astari Dianty, Antonius Bimo Rentor et al.· International Seminar on Int...· 0 citations
Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh et al.· Risks· 0 citations
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