Using advanced artificial intelligence systems employed by the US federal tax authority (IRS) and the new Unified Corruption Cases Registry, the government is accelerating efforts to enhance transparency and combat tax evasion. The goal is transitioning from random sampling audits to a predictive risk analysis model with automated tools. The new strategy’s core element is massive data analysis using specialized algorithms and automatic cross-referencing of information, making audits more targeted and immediate.
Artificial intelligence systems can process millions of tax returns, compare behaviors, and identify deviations indicating higher tax evasion probability. Each case receives a risk score and proceeds for audit based on specific criteria, rather than random selection.
Particular emphasis is placed on cases involving freelancers and businesses with unusually high expenses, recurring losses, or large gaps between declared income and actual financial situation. Algorithms identify patterns deviating from industry averages and trigger alerts for further investigation by auditors. Simultaneously, automatic cross-referencing takes priority by leveraging data from banks, insurance companies, investment products, and international organizations for information exchange. This approach reduces margins for concealing income and suspicious transactions, while prioritizing cases with the greatest tax interest.
The new Unified corruption cases registry
Meanwhile, a new Unified Corruption Cases Registry is being established through cooperation between the Ministries of Justice, National Economy, and Digital Governance. The Registry will function as a central database tracking the progress of all related cases, from initial investigation stages to final case closure.
According to relevant provisions, the Registry will include offenses such as bribery and corruption of officials and employees, fraud and embezzlement against the State or European Union financial interests, abuse of power, money laundering from criminal activities, and match-fixing offenses. Registration of cases connected to grants, cross-border fraud, and corporate liability is also planned.
Data will be entered by judicial authorities, oversight bodies, and services including the Independent Authority for Public Revenue, National Transparency Authority, and prosecutor’s offices. Each case will receive a unique code, while all maintained data will be anonymous. The Registry will generate statistical data on delays, crime types, and audit results to identify system weaknesses and propose corrective interventions. Government officials estimate that combining digital tools with central oversight can improve audit effectiveness and accelerate justice delivery. The next step will be connecting individual databases and gradually automating high-priority case selection.
