Jul 2026· International Seminar on Intelligent Technology and Its Applications· pp. 244-249· 0 citations· 17 references
Abstract
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.
This work provides a mathematically grounded benchmarking framework for integrating Explainable Artificial Intelligence (XAI) into fraud detection pipelines, aligning high-accuracy analytics with the transparency requirements expected in regulated financial environments.
Henrique Barros, F. Antunes, Maryam Abbasi· International Conference on...· 0 citations
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.
Kanchan, Meenu Gupta, Rakesh Kumar et al.· International Conference on...· 0 citations
The study contributes an auditor-oriented architecture that separates predictive screening from the professional conclusion, embeds explanation quality and calibration into model evaluation, and maps model outputs to review procedures, suitable for future validation on verified Indian firm-year enforcement, restatement, and qualified-report outcomes.
Mohammed Abid, S. Kothari· International Journal For Mu...· 0 citations
A novel Ranked Attribute Selection with Midpoint Filtering with Midpoint Filtering (RASF) framework that extends LCS (EXTRACS) to enhance feature selection and rule validation for credit approval and supports explainable AI in credit scoring.
M. Ahamed, Abubakar Siddique, Trung Nguyen et al.· Proceedings of the Genetic a...· 0 citations
This work compared five machine learning classifiers on a loan approval dataset: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine and applied SHAP TreeExplainer to interpret the best-performing model.
Shengze Xu· Advances in Economics, Manag...· 0 citations
Large volumes of loan applications motivate automated decision-support systems that can reduce processing delays and improve consistency while controlling credit risk. This study presents a unified supervised-learning framework comparing XGBoost, Gradient Boosting, and CatBoost for loan approval prediction. The experiments use the Dream Housing Finance dataset containing 614 applications and 12 predictive variables after removing Loan_ID. The pipeline includes missing-value treatment, feature engineering, scaling, SMOTE-based class balancing applied only to training data, and evaluation on a held-out test set of 169 samples. Perfect training performance is treated as a diagnostic warning rather than evidence of generalization. CatBoost achieved the best held-out accuracy (88.17%), precision (88.37%), recall (88.37%), and F1-score (88.37%), with 10 false approvals and 10 false rejections. Confusion-matrix analysis, false-positive and false-negative rates, balanced accuracy, and Wilson confidence intervals indicate the most balanced performance among the evaluated models. The framework is intended as a prototype decision-support approach; larger multi-institutional validation, probability-based discrimination analysis, explainability, calibration, and fairness assessment are required before deployment in real lending environments.
N. Dandotiya, Kirti Jain, Prashant Kumar Shrivastava· 2026 International Conferenc...· 0 citations
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