GenRocket Trains AI-Assisted Tax Fraud Detection System

In our global economy, VAT fraud and sales tax evasion has become a major source of lost revenue for governments around the world. That’s because it’s difficult to spot unpaid taxes within the massive volume of commercial transactions flowing through multinational supply chains. Each participant in the supply chain may be subject to different tax requirements based on where the transaction take place. As a result, tax fraud has become widespread problem that is increasing in scope every year.

To help governments in more than 160 countries detect tax fraud, a major software company developed artificial intelligence technology for a sophisticated tax fraud detection system. Identifying tax fraud requires highly accurate anomaly detection algorithms – a challenge machine learning models and artificial intelligence systems can overcome if properly tested and trained with a huge volume of controlled and conditioned synthetic data.

In our latest case study, learn how GenRocket’s Test Data Automation platform was used to generate synthetic training data containing 990,000 transactions for 1,440,090 organizations to cover the tax provisions for all supply chain scenarios. The solution demonstrates how GenRocket’s rules-based synthetic training data can be an extremely effective choice for anomaly detection, not only for fraud detection, but for a wide range of applications in financial services, healthcare, cybersecurity, manufacturing, and many others.