This study proposes TRuE-XAI (Transparent, Rule-based, and Explainable Artificial Intelligence), an integrated framework combining imbalance-aware ensemble learning, automated hyperparameter optimization, rule-based explainability, visual analytics, and causal inference for transparent earnings-growth forecasting.
Abstract
Forecasting corporate earnings growth is fundamental to investment, credit, and regulatory decision-making. Existing forecasting approaches either rely on restrictive linear assumptions or provide limited interpretability, making them less suitable for high-stakes financial applications. This study proposes a transparent and causally informed framework for predicting future corporate earnings growth from financial statement data. We present TRuE-XAI (Transparent, Rule-based, and Explainable Artificial Intelligence), an integrated framework combining imbalance-aware ensemble learning, automated hyperparameter optimization, rule-based explainability, visual analytics, and causal inference. Random Forest, XGBoost, and LightGBM classifiers were optimized using Optuna and Hyperopt and evaluated with multiple class-balancing strategies, including SMOTE, ADASYN, TomekLinks, and Repeated Edited Nearest Neighbours (RENN). Experiments were conducted on real-world SEC-derived quarterly financial statement data from U.S. publicly listed firms covering 2014–2024. Model transparency was achieved through Anchor explanations, multi-metric feature-importance analysis, SilVA visual analytics, and causal effect estimation using DoWhy and EconML. The best-performing configuration, TomekLinks–XGBoost, achieved an F1-score of 0.467 and accuracy of 0.849 on the real SEC dataset while maintaining stable generalization under a leakage-free evaluation protocol. Anchor explanations generated concise, high-precision IF–THEN rules that explained individual predictions, whereas complementary feature-importance analyses identified consistent financial drivers across models. Causal inference showed that Net Profit Margin Change, Sales Growth, EBIT, and Asset Turnover exert positive causal effects on the probability of future earnings growth, while Inventory to Total Assets and Cash Flow to Net Income exhibited negative causal effects. Placebo and refutation tests supported the robustness of the estimated treatment effects. TRuE-XAI integrates predictive modelling, explainable AI, visual analytics, and causal inference into a unified framework for transparent earnings-growth forecasting. By combining competitive predictive performance with interpretable decision rules and causally grounded insights, the framework provides a practical and trustworthy approach for financial decision support and demonstrates how explainable and causal machine learning can be applied in regulated financial environments.
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
Corporate financial distress imposes sizable and persistent costs on shareholders, creditors, employees, and the broader economy. However, practical risk governance in emerging markets requires not only accurate risk ranking but also reliable probabilities that can be translated into monitoring thresholds and escalation actions. This study develops an early-warning framework for Vietnamese listed non-financial firms that targets decision-useful one-year-ahead distress probabilities. It evaluates whether calibrated and explainable machine-learning models remain operationally usable under temporal change. The analysis uses a firm-year panel of companies listed on the Ho Chi Minh City Stock Exchange and the Hanoi Stock Exchange over 2014–2024, comprising 7305 observations. A strict chronological design is implemented to emulate forward deployment: model estimation uses 2015–2019, tuning and probability calibration use 2020–2021, and final evaluation is conducted once on an out-of-time hold-out period of 2022–2024. During the test period, distress prevalence increases to 15.42%, compared with approximately 12% in earlier windows. A conventional probabilistic benchmark is compared with multiple machine-learning classifiers under an identical feature space and temporal protocol. An explanation layer is also applied to support governance-oriented interpretation. Out-of-time evidence indicates that tree-ensemble methods provide the strongest combination of ranking performance and probability accuracy, with the random forest providing the best out-of-time performance among the evaluated models, with reasonable discrimination and the lowest probability error, although the magnitude of the AUC improvement should be interpreted as moderate rather than exceptional. The random forest’s calibrated probabilities support monotonic risk stratification and transparent, capacity-constrained watchlists. Selecting the top 10% of firm-years by predicted risk captures 48.3% of distress events with 44.3% precision, corresponding to a 2.87-fold lift over the base rate. Incremental gains from the structural, market-implied distance-to-default proxy are limited once standard accounting and market measures are included. This suggests that routinely available accounting and market variables already capture most of the relevant distress information in this setting. Overall, the results support an out-of-time calibrated and explainable pipeline as a practical foundation for auditable monitoring and tiered escalation in Vietnam’s listed corporate sector.
Tuyen Le Nam, Tam Phan Huy· Journal of International Com...· 0 citations
This study explores the use of Explainable Artificial intelligence techniques to improve the interpretability of credit default prediction and highlights the practical value of explainable machine learning in developing more understandable, trustworthy, and accountable credit risk assessment systems for real-world financial decision-making.
Muskan, B. Sidhu· International Journal of Com...· 0 citations
QFRS underpins a public state-of-the-art leaderboard, ensuring that only studies satisfying these standards are ranked, with the goal of shifting the literature from opaque, error-metric-driven results to transparent, economically meaningful and comparable benchmarks.
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