This is like having a very smart auditor that continuously watches tax records, bank-like transaction trails, and filing patterns to spot who might be under-reporting income or committing tax fraud, and then alerts tax officers to investigate those specific cases first.
Manual tax audits are slow, random, and resource-intensive, so many fraud cases slip through while compliant citizens are over-scrutinized. An AI-based monitoring system automatically flags high‑risk taxpayers and suspicious transactions, improving detection of evasion while reducing unnecessary audits.
The defensibility would come from access to large-scale, high-quality historical tax and transaction data, integration with government IT systems, and domain-specific fraud rules and labels that improve models over time.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data privacy and secure access to tax, banking, and third-party financial records; plus potential model drift as fraudsters change behavior.
Early Majority
Focus on citizen-level tax compliance monitoring and fraud scoring using AI on structured tax and financial data, tailored for public-sector tax authorities rather than generic financial fraud detection or commercial risk scoring.