{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:05:12Z","timestamp":1789362312473,"version":"build-2803163510"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Information asymmetry between bank managers and external stakeholders is a common relationship between financial-statement fraud and banking instability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u20132025).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdata.2026.1914133","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T04:35:36Z","timestamp":1789360536000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Voluntary disclosure, banking stability, and AI-augmented forensic accounting: an exploratory econometric and machine-learning study of Palestinian banks"],"prefix":"10.3389","volume":"9","author":[{"given":"Bahaa Subhi","family":"Razia","sequence":"first","affiliation":[{"name":"Department of Industrial and Logistics Management, Palestine Technical University \u2013 Kadoorie","place":["Tulkarm, Palestine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najwan Ibrahim","family":"Jadallah","sequence":"additional","affiliation":[{"name":"Department of Computerized Banking and Financial Science, Faculty of Business and Economics, Palestine Technical University \u2013 Kadoorie","place":["Tulkarm, Palestine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qasim","family":"Zureigat","sequence":"additional","affiliation":[{"name":"Sulaiman AlRajhi School of Business, Sulaiman AlRajhi University","place":["Al Bukayriyah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reem","family":"Khamis","sequence":"additional","affiliation":[{"name":"Department of Business Administration, University College of Bahrain","place":["Manama, Bahrain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bahaa Subhi","family":"Awwad","sequence":"additional","affiliation":[{"name":"Department of Computerized Banking and Financial Science, Faculty of Business and Economics, Palestine Technical University \u2013 Kadoorie","place":["Tulkarm, Palestine"]},{"name":"Department of Accounting, Finance and Banking, College of Business and Finance, Ahlia University","place":["Manama, Bahrain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"445","DOI":"10.19030\/jabr.v30i2.8416","article-title":"The use of CAMELS in detecting financial distress of Islamic banks in Malaysia","volume":"30","author":"Abdul Rahman","year":"2014","journal-title":"J. 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