Connecting tax-risk analysis to officer action.
Data, rules and machine learning, connected to the people responsible for audit selection.
- Programme funder
The challenge
Income-tax and sales-tax records contain different signals of potential non-compliance. Those signals need to be reconciled, evaluated and made useful to the officers choosing cases for further scrutiny.
The assignment brought rule-based analysis and machine-learning approaches into a common decision workflow. Technical work translated tax-domain questions into risk use cases, supported validation with domain officers and addressed how the system would be used by technical teams and field formations. Training and engagement with PRAL formed part of the delivery.
Contribution and outputs
Through Adam Smith International’s ReMIT programme, Mahdi Khan led technical work supporting FBR’s Compliance Risk Management Wing and worked with PRAL on training and technical handover.
- Rule-based and machine-learning risk use cases
- Technical support for audit-case prioritisation
- Domain review of identified signals
- Training and technical engagement with PRAL
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