A Decoupling-Fusion System for Financial Fraud Detection: Operationalizing Causal–Temporal Asynchrony in Multimodal Data

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–Temporal 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–Chronic 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’s A-share market (2001–2021) 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.