Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks (1908.00734v1)

Abstract: The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. In contrast, more advanced approaches inspired by the recent success of deep learning often lack seamless interpretability of the detected results. To overcome this challenge, we propose the application of adversarial autoencoder networks. We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries. The learned representation provides a holistic view on a given set of journal entries and significantly improves the interpretability of detected accounting anomalies. We show that such a representation combined with the networks reconstruction error can be utilized as an unsupervised and highly adaptive anomaly assessment. Experiments on two datasets and initial feedback received by forensic accountants underpinned the effectiveness of the approach.

• The paper introduces an AAE-based method that combines autoencoders and GANs to learn meaningful ERP data representations for anomaly detection.
• It employs a dual-phase training process integrating reconstruction error and adversarial regularization to identify both global and local anomalies.
• Experiments on synthetic and real-world datasets demonstrate enhanced interpretability and adaptability in financial fraud detection.

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks

The paper "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks" presents an innovative approach for detecting fraud in accounting data through the application of Adversarial Autoencoder (AAE) neural networks. This method addresses the limitations of current anomaly detection techniques in financial statement audits, which heavily rely on predefined rules that fraudsters often circumvent and the opacity in interpretability associated with more advanced deep learning methods.

Methodology

The researchers propose leveraging AAEs to detect accounting irregularities by learning meaningful representations of journal entries archived in Enterprise Resource Planning (ERP) systems. The architecture combines the advantages of Autoencoder Neural Networks (AENs) and Generative Adversarial Networks (GANs) to impose arbitrary prior distributions on the latent space, facilitating the discernment of intrinsic journal entry characteristics. The encoded representations are then utilized to distinguish global and local anomalies autonomously.

In the training process, the AAEs are instructed through a dual-phase process: a reconstruction phase to minimize input and output dissimilarities, and an adversarial regularization phase where a discriminator network distinguishes real from prior-imposed fake distributions. The Gaussian mixture model prior aids in decomposing the latent space into semantically rich clusters, enabling an auditor to contextualize detected anomalies within familiar accounting norms.

Results and Implications

Experiments performed on both synthetic and real-world datasets exhibit that the AAE architecture effectively partitions accounting data into semantically significant regions while uncovering anomalous entries based on a proposed anomaly score. This score integrates the entry's reconstruction error - indicative of unusual attribute co-occurrences - and the divergence from coded latent space modes - suggestive of rare individual attribute values. The balance between these features can be adjusted according to the anomaly type being prioritized.

The findings demonstrate that AAEs enhance interpretability, offering a broader framework for reviewing transactional data comprehensively and progressively. This novel approach complements existing audit techniques, providing robust detection mechanisms adaptable to evolving fraud scenarios without explicit supervision.

Future Directions

The potential applications of AAEs in auditing extend beyond anomaly detection to encompass more comprehensive and interpretable data sampling strategies. Future exploration may focus on enhancing latent space disentanglement and rapid adaptation to diverse accounting databases. Given the scale and complexity of modern digital accounting systems, the scalable implantation of such AI-driven models could profoundly impact the efficiency and accuracy of audits, ultimately fortifying financial integrity standards.

In conclusion, this paper provides significant insights into applying deep learning architectures to detect accounting anomalies and underscores the importance of adaptive machine learning solutions in tackling fraud in financial ecosystems. The methodology serves as a pioneering addition to audit technology, reflecting a significant step towards the automation and sophistication of fraud detection in accounting.