This prototype demonstrates how risk scoring can support audit selection using realistic administrative inputs rather than idealized data environments.
The design prioritizes interpretability, scalability, and workflow fit.
PROTOTYPE
Audit Risk Model Prototype
A machine learning-based framework for taxpayer risk profiling in low-data environments.
Interactive Flow
Input operational signals to estimate an audit risk score.
This prototype mirrors how a tax administration might combine revenue, sector, and filing behavior into a practical audit prioritization model.
Output
Risk Score
Provide the three inputs on the left to generate a sample risk classification of Low, Medium, or High.