GRThe Greek tax authority, the Independent Authority for Public Revenue (AADE), has transitioned from traditional methods like complaint-based audits and random sampling to a data-driven approach for targeting businesses for tax inspections. This shift involves analyzing vast amounts of digital data from electronic books (myDATA), tax returns, connected cash registers, POS terminals, and online platforms to identify potential tax risks. By comparing these data sources, AADE aims to detect patterns indicative of non-compliance rather than focusing on individual violations. Key indicators include discrepancies between reported income and actual transactions, failure to integrate payment systems, incomplete documentation, and inconsistencies across different data sets. Advanced algorithms analyze this information to generate risk scores and flag businesses for further scrutiny.
Bias read (Center): The article presents a factual description of AADE’s operational methodology without overtly criticizing or praising the system. It explains the technical processes and goals of the tax authority in a neutral tone, focusing on how data analysis improves efficiency and accuracy in detecting tax risks
