Frontiers in big data · 2026
Voluntary disclosure, banking stability, and AI-augmented forensic accounting: an exploratory econometric and machine-learning study of Palestinian banks.
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Razia Bahaa Subhi, Jadallah Najwan Ibrahim, Zureigat Qasim, Khamis Reem, Awwad Bahaa Subhi
Аннотация
INTRODUCTION: Information asymmetry between bank managers and external stakeholders is a common relationship between financial-statement fraud and banking instability.
METHODS: This study combines an AI-augmented forensic accounting framework with voluntary disclosure, banking stability indicators, and exploratory machine learning (ML) approaches. The study integrates fixed-effects regression with Logistic Regression, Random Forest, XGBoost, Isolation Forest, and SHAP-based explainability using panel data from the whole population of seven banks listed on the Palestine Exchange (2019-2025).
RESULTS: According to the econometric results, there is a conditional rather than a uniform relationship between voluntary disclosure and financial stability, with variation by bank size, age, and leverage. Additionally, the exploratory machine-learning analyses indicate that nonlinear approaches could help find unusual bank-year records and instability-risk patterns that are not fully captured by traditional linear models. SHAP analysis enhanced the interpretability of model classifications, and ensemble approaches outperformed Logistic Regression in cross-validation within this small sample. The machine-learning results are considered as exploratory proof-of-concept evidence rather than externally confirmed predictive outcomes due to the small sample size and lack of independently verified fraud labels.
DISCUSSION: Overall, the study shows how AI-augmented forensic accounting can enhance supervisory prioritization, instability-risk screening, and the expert assessment of anomalous observations in institutionally unstable banking contexts, thereby complementing traditional econometric analysis.
METHODS: This study combines an AI-augmented forensic accounting framework with voluntary disclosure, banking stability indicators, and exploratory machine learning (ML) approaches. The study integrates fixed-effects regression with Logistic Regression, Random Forest, XGBoost, Isolation Forest, and SHAP-based explainability using panel data from the whole population of seven banks listed on the Palestine Exchange (2019-2025).
RESULTS: According to the econometric results, there is a conditional rather than a uniform relationship between voluntary disclosure and financial stability, with variation by bank size, age, and leverage. Additionally, the exploratory machine-learning analyses indicate that nonlinear approaches could help find unusual bank-year records and instability-risk patterns that are not fully captured by traditional linear models. SHAP analysis enhanced the interpretability of model classifications, and ensemble approaches outperformed Logistic Regression in cross-validation within this small sample. The machine-learning results are considered as exploratory proof-of-concept evidence rather than externally confirmed predictive outcomes due to the small sample size and lack of independently verified fraud labels.
DISCUSSION: Overall, the study shows how AI-augmented forensic accounting can enhance supervisory prioritization, instability-risk screening, and the expert assessment of anomalous observations in institutionally unstable banking contexts, thereby complementing traditional econometric analysis.