In an era where data is the new "oil" of public administration, the Independent Authority for Public Revenue (AADE) is moving forward with a radical restructuring of how it handles overdue debts. The transition from horizontal enforcement measures to a personalized assessment system, based on Artificial Intelligence and Big Data analysis, marks the end of an entire era for the Greek tax system.
From Horizontal Suppression to Personalized Assessment
For decades, the Greek tax administration operated in a rather rigid manner: anyone who owed money was treated as a potential offender, regardless of their history or financial reality. The new model being activated by AADE aims to overturn this assumption. Using advanced machine learning algorithms, the Authority will now be able to "X-ray" the profile of each debtor, categorizing them based on their behavior and actual ability to pay.
The central question the system is called to answer is simple but critical: "Can't pay or won't pay?". The answer to this question will now determine the tax office's stance. For those characterized as having "genuine inability," the system will propose settlement solutions and facilitation. Conversely, for "strategic defaulters"—those who have the financial capacity but choose not to fulfill their obligations—measures will be immediate, strict, and automated.
The Profile of a "Strategic Defaulter"
But how is a strategic defaulter defined in the eyes of an algorithm? AADE will draw data from a wide range of sources: bank accounts, credit card movements, real estate, company participations, and even living expenses that do not align with declared incomes. The system will look for behavioral patterns, such as transferring funds to third parties just before deadlines or systematically avoiding payments despite having liquidity.
The use of AI allows for the creation of a "Compliance Score" for each taxpayer. This score will not just be about creditworthiness, as is the case with banks, but about "tax compliance." A taxpayer with a high compliance score who suddenly fails to pay an installment will be treated with leniency, as the algorithm will recognize it as an extraordinary difficulty. In contrast, someone showing a systematic tendency to hide income will immediately trigger the system's "red lights."
Big Data and Cross-Referencing: The End of Tax Evasion as We Knew It?
The power of the new system lies in its ability to perform millions of cross-references in real-time. It is no longer about sample audits but continuous monitoring of economic activity. Information from the Taxisnet system will be combined with data from the Land Registry, EFKA (Social Security), banks, and even short-term rental platforms or social networks in cases of extensive audits.
"Technology allows us to be fair. Justice means not exhausting the severity of the law on someone struggling to survive, but being relentless with those who exploit the system at the expense of others," say AADE sources.
However, this digital omnipotence also raises serious questions about personal data protection and the possibility of algorithmic errors. What happens when the AI makes a mistake? How can a citizen challenge a decision made by a "black box" algorithm? The transparency of assessment criteria remains one of the major issues of this new era.
Social Justice or Digital Panopticon?
The implementation of the new model is a bet for the Greek government and AADE. On one hand, increasing public revenue by cracking down on strategic inconsistency is essential for fiscal stability and reducing tax rates for the compliant. On the other hand, creating a punitive state that monitors every economic breath of its citizens could lead to social alienation.
The key to success lies in balance. Artificial Intelligence should not replace human judgment but assist it. AADE must ensure that the new system operates with absolute transparency and that there are safeguards to protect citizens' rights. The transition to digital tax administration is inevitable, but it must also be human-centric.
