Fraud continues to be a major threat to industry and government. Traditionally, organizations have focused on…
Category: Accounting Intelligence
Data Mining Journal Entries for Fraud Detection: An Exploratory Study
Fraud detection has become a critical component of financial audits and audit standards have heightened emphasis…
A Business Process Mining Application for Internal Transaction Fraud Mitigation
Internal fraud has received a great deal of attention from interested parties like governments or non-profit…
Auditing Journal Entries Using Extreme Value Theory
The area of journal entries is deemed to pose a high risk of material misstatements to…
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…