Audit Risk Model
Machine learning-based taxpayer risk identification.
- • Financial ratios
- • Filing patterns
- • Sectoral benchmarks
Data & Tools
Analytical tools and conceptual models for tax administration and public finance.
Maitras.ai develops analytical tools and conceptual models designed to support governments and institutions in applying data science to tax administration and public finance.
Introduction
Structured tools designed to be conceptually robust, scalable, and adaptable to low-data environments.
These tools translate Maitras.ai research into frameworks that governments and institutions can use to improve analysis, forecasting, risk targeting, and economic visibility.
Tool Principles
Built for fragmented systems, uneven data quality, and public-sector use cases.
Maitras.ai tools are designed to remain analytically disciplined while staying usable in operational settings where institutional and data constraints are real.
Conceptually robust
Models are grounded in clear analytical logic rather than black-box presentation.
Scalable
Frameworks can grow from concept notes into larger country diagnostics and system modules.
Low-data ready
Adapted for environments where data is incomplete, systems are fragmented, and analytical capacity is limited.
Tools and Frameworks
Three flagship models define the current tool layer.
These frameworks correspond directly to the document: audit risk targeting, revenue forecasting under uncertainty, and the Gender Economic Visibility Index.
Revenue Forecasting Framework
Estimating government revenue under uncertainty.
- • Macroeconomic indicators
- • Commodity price scenarios
- • Behavioral responses
GEVI
A composite index for women’s economic visibility.
- • Financial inclusion
- • Labor participation
- • Enterprise ownership
- • Data representation
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Jun 2026
Gender Economic Visibility Index
A composite index measuring how visibly women are represented across economic and data systems.
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Jun 2026
Revenue Forecasting Framework
A structured model for estimating public revenue under uncertainty using macro and sector-specific inputs.
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Jun 2026
Audit Risk Model Prototype
A machine learning-based framework for taxpayer risk profiling in low-data environments.