{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:03:19Z","timestamp":1778947399884,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72271155"],"award-info":[{"award-number":["72271155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eastern Talent Plan of Shanghai"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Financial statement fraud is a socio-technical risk that arises from coupled organizational, informational, and regulatory processes. To address the Identification Paradox in financial fraud detection, where existing models cannot simultaneously recognize both chronic manipulation and acute outbreaks in financial data, this study proposes the Causal\u2013Temporal Asynchrony (CTA) theory as a process-oriented conceptual framework that guides feature construction and model design in a predictive setting. CTA defines fraud motive as a chronic, multi-period accumulation and fraud action as an acute, single-year event. To operationalize CTA within a predictive setting, we build a deployable Decoupling-Fusion System that encodes CTA as an Acute\u2013Chronic Binary Feature Dimensions schema and performs detection via Decoupling-Fusion FraudNet. Within this system, parallel Long Short-Term Memory networks (LSTM) capture chronic motive signals from longitudinal sequences, while parallel Convolutional Neural Networks (CNN) and a Feed-forward Neural Network (FNN) identify acute action signals from multimodal snapshots; the resulting asynchronous probabilities are integrated via an adaptive decision-level fusion mechanism. Empirical tests on China\u2019s A-share market (2001\u20132021) show the system (AUC = 0.967) outperforms baseline models. Furthermore, eXplainable AI analysis reveals patterns consistent with the classic fraud triangle (pressure, opportunity and rationalization). This study develops a theory-grounded decision-support system that unifies acute and chronic evidence streams and provides a deployable blueprint for continuous auditing and governance.<\/jats:p>","DOI":"10.3390\/systems14010025","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:50:21Z","timestamp":1766710221000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Decoupling-Fusion System for Financial Fraud Detection: Operationalizing Causal\u2013Temporal Asynchrony in Multimodal Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Wenjuan","family":"Li","sequence":"first","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinghua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2729-480X","authenticated-orcid":false,"given":"Ziyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zulei","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxian","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3170-4073","authenticated-orcid":false,"given":"Shugang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104185","DOI":"10.1016\/j.frl.2023.104185","article-title":"The Spillover Effect of Corporate Frauds and Stock Price Crash Risk","volume":"57","author":"Wen","year":"2023","journal-title":"Financ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1108\/AAAJ-12-2023-6792","article-title":"Tone at the Top, Corporate Irresponsibility and the Enron Emails","volume":"37","author":"Rahaman","year":"2024","journal-title":"Accounting Audit. Account. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100629","DOI":"10.1016\/j.jbef.2022.100629","article-title":"The Luckin Coffee Scandal and Short Selling Attacks","volume":"34","author":"Peng","year":"2022","journal-title":"J. Behav. Exp. Financ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1016\/j.irfa.2023.102827","article-title":"Detecting financial statement fraud using dynamic ensemble machine learning","volume":"89","author":"Achakzai","year":"2023","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Al-Daoud, K.I., and Abu-AlSondos, I.A. (2025). Robust AI for Financial Fraud Detection in the GCC: A Hybrid Framework for Imbalance, Drift, and Adversarial Threats. J. Theor. Appl. Electron. Commer. Res., 20.","DOI":"10.3390\/jtaer20020121"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106883","DOI":"10.1016\/j.asoc.2020.106883","article-title":"Ensemble of deep sequential models for credit card fraud detection","volume":"99","author":"Forough","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TCSS.2022.3158318","article-title":"Time-aware attention-based gated network for credit card fraud detection by extracting transactional behaviors","volume":"10","author":"Xie","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1007\/s10796-022-10346-6","article-title":"Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework","volume":"25","author":"Hajek","year":"2023","journal-title":"Inf. Syst. Front."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.eswa.2018.09.039","article-title":"Bankruptcy prediction using imaged financial ratios and convolutional neural networks","volume":"117","author":"Hosaka","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_10","unstructured":"Cressey, D.R. (1953). Other People\u2019s Money: A Study in the Social Psychology of Embezzlement, Free Press."},{"key":"ref_11","first-page":"557","article-title":"Earnings Management and Capital Resource Allocation: Evidence from China\u2019s Accounting-Based Regulation of Rights Issues","volume":"21","author":"Chen","year":"2004","journal-title":"Contemp. Account. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10551-024-05628-9","article-title":"Academic Fraud and Remote Evaluation of Accounting Students: An Application of the Fraud Triangle","volume":"195","author":"Bierstaker","year":"2024","journal-title":"J. Bus. Ethics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.jaccpubpol.2015.05.006","article-title":"The effect of alternative fraud model use on auditors\u2019 fraud risk judgments","volume":"34","author":"Boyle","year":"2015","journal-title":"J. Account. Public Policy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"231","DOI":"10.2308\/aud.2008.27.2.231","article-title":"Financial statement fraud: Insights from the academic literature","volume":"27","author":"Hogan","year":"2008","journal-title":"Audit. A J. Pract. Theory"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1017\/S0022109000004221","article-title":"The Cost to Firms of Cooking the Books","volume":"43","author":"Karpoff","year":"2008","journal-title":"J. Financ. Quant. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2469\/faj.v55.n5.2296","article-title":"The Detection of Earnings Manipulation","volume":"55","author":"Beneish","year":"1999","journal-title":"Financ. Anal. J."},{"key":"ref_17","first-page":"102596","article-title":"Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization","volume":"55","author":"Rtayli","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"120760","DOI":"10.1016\/j.eswa.2023.120760","article-title":"Fraud detection in capital markets: A novel machine learning approach","volume":"231","author":"Yi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114037","DOI":"10.1016\/j.dss.2023.114037","article-title":"Efficient fraud detection using deep boosting decision trees","volume":"175","author":"Xu","year":"2023","journal-title":"Decis. Support Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.3390\/jtaer18040110","article-title":"A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research","volume":"18","author":"Zhang","year":"2023","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116429","DOI":"10.1016\/j.eswa.2021.116429","article-title":"Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances","volume":"193","author":"Hilal","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.jacceco.2008.02.003","article-title":"Annual Report Readability, Current Earnings, and Earnings Persistence","volume":"45","author":"Li","year":"2008","journal-title":"J. Account. Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1111\/j.1540-6261.2010.01625.x","article-title":"When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks","volume":"66","author":"Loughran","year":"2011","journal-title":"J. Financ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100682","DOI":"10.1016\/j.accinf.2024.100682","article-title":"Accounting Fraud Detection Using Contextual Language Learning","volume":"53","author":"Bhattacharya","year":"2024","journal-title":"Int. J. Account. Inf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal Machine Learning: A Survey and Taxonomy","volume":"41","author":"Ahuja","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113421","DOI":"10.1016\/j.dss.2020.113421","article-title":"Deep learning for detecting financial statement fraud","volume":"139","author":"Craja","year":"2020","journal-title":"Decis. Support Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100744","DOI":"10.1016\/j.accinf.2025.100744","article-title":"Bankruptcy prediction: Integration of convolutional neural networks and explainable artificial intelligence techniques","volume":"56","author":"Lin","year":"2025","journal-title":"Int. J. Account. Inf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.dss.2010.11.006","article-title":"Detection of financial statement fraud and feature selection using data mining techniques","volume":"50","author":"Ravisankar","year":"2011","journal-title":"Decis. Support Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating causal relations by econometric models and cross-spectral methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1037\/0033-2909.103.3.411","article-title":"Structural equation modeling in practice: A review and recommended two-step approach","volume":"103","author":"Anderson","year":"1988","journal-title":"Psychol. Bull."},{"key":"ref_31","unstructured":"Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques, MIT Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1745-9125.1992.tb01093.x","article-title":"Foundation for a General Strain Theory of Crime and Delinquency","volume":"30","author":"Agnew","year":"1992","journal-title":"Criminology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2307\/1879431","article-title":"The Market for \u2018Lemons\u2019: Quality Uncertainty and the Market Mechanism","volume":"84","author":"Akerlof","year":"1970","journal-title":"Q. J. Econ."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/1\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T01:17:06Z","timestamp":1766711826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/1\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,25]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["systems14010025"],"URL":"https:\/\/doi.org\/10.3390\/systems14010025","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,25]]}}}