The area of journal entries is deemed to pose a high risk of material misstatements to…
Month: December 2020
Visual Exploration of Journal Entries to Detect Accounting Irregularities and Fraud
Fraud investigators have recently recognized the importance of data visualization for fraud detection. Data visualization is…
Detection of Anomalies in Large-Scale Accounting Data using Deep Autoencoder Networks
Deep autoencoder neural networks can be used to detect anomalous journal entries. Experiments on two real-world…
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
The Association of Certified Fraud Examiners estimates in its “GlobalStudy on Occupational Fraud and Abuse 2018”…
Adversarial Learning of Deep fakes in Accounting
Machine learning techniques, and in particular deep neural networks, created advances across a diverse range of…
Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data
Research has shown that little attention is being paid to Value Added Tax (VAT) issues. There…
Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture
Deep learning is used to learn machines how to reconstruct journal entries. We developed basic models…
A Semi-Supervised Machine Learning Approach to Detect Anomalies in Big Accounting Data
Anomaly detection in large scale accounting data is one of the long-standing challenges in the financial…
Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits
Most public companies are required by law to have their financial statements audited annually by external…
Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like…