Deep Sequence Models For Real-Time Journal Entry Anomaly Detection In Financial Reporting

Abstract

Financial reporting integrity remains a critical concern for auditors, regulatory bodies,  and stakeholders as the complexity and volume of accounting transactions continue to escalate.  Traditional rule-based audit sampling techniques face increasing challenges in identifying  fraudulent activities and accounting errors concealed within massive general ledger datasets. This  research explores the application of deep sequence models, particularly Long Short-Term Memory  (LSTM) networks and their variants, for real-time anomaly detection in journal entry data. By  treating journal entries as temporal sequences and leveraging the memory capabilities ofrecurrent architectures, our approach captures intricate patterns and dependencies that  characterize normal accounting behavior. The proposed framework addresses key challenges  including variable-length transaction structures, class imbalance in anomaly datasets, and the need for unsupervised or semi-supervised learning paradigms where labeled fraudulent instances  remain scarce. We demonstrate how LSTM's sophisticated gating mechanisms enable selective  information retention across extended temporal horizons, while stacked architectures learnhierarchical representations of transaction patterns. The methodology integrates preprocessing  techniques to handle categorical and numerical features, followed by deep learning architectures  that model both short-term transactional patterns and long-range dependencies across accounting  periods. Experimental validation using synthetic and real-world financial datasets shows that  sequence-based deep learning models achieve superior performance metrics compared to  conventional statistical methods and shallow machine learning techniques, with particular  improvements in separating anomalous transactions based on reconstruction error distributions.  The reconstruction-based detection paradigm demonstrates clear discrimination between normal  entries exhibiting low reconstruction errors and fraudulent entries with significantly elevated  error magnitudes. This work contributes to the growing intersection of artificial intelligence and  forensic accounting by providing a scalable, adaptable solution for automated anomaly detection in enterprise financial systems.