Tax Fraud Detection
This application area focuses on automatically identifying potentially fraudulent or non-compliant tax returns and transactions submitted by individuals and businesses. Instead of relying solely on manual, random, or rules-based audits, models analyze large volumes of historical tax filings, payment records, and third‑party data to detect patterns indicative of underreporting, false claims, or other evasion tactics. It matters because tax fraud and evasion erode government revenue, strain public finances, and create unfairness between honest and dishonest taxpayers. By prioritizing high‑risk cases for review, these systems help tax authorities recover lost revenue, reduce the burden of unnecessary audits on compliant citizens, and allocate auditors’ time more effectively. In practice, AI is used to generate risk scores for each return, flag anomalous behavior, and continuously refine detection models as new fraud patterns emerge.
The Problem
“ML risk scoring to prioritize tax fraud audits with fewer false positives”
Organizations face these key challenges:
Audit selection is random or rules-heavy, leading to low hit rates and wasted investigator time
Fraud patterns change yearly, causing rule drift and missed schemes
High false-positive rates create taxpayer friction and political risk