Quantitative framework for scoring and tiering AI model risk across complexity, data quality, model impact, and regulatory exposure dimensions.
A structured framework for assessing and scoring the risk of AI/ML models in financial institutions. Uses a multi-dimensional scoring approach covering model complexity, data quality, business impact, regulatory exposure, and explainability. Produces a composite risk score that drives validation intensity and governance requirements.
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This Business Requirements Document (BRD) outlines the functional and non-functional requirements for implementing an enterprise-grade AI/ML solution.
The document covers scope definition, stakeholder analysis, regulatory alignment, and technical architecture considerations.
2.1 In-Scope: Core platform capabilities, data ingestion pipelines, model governance workflows, and regulatory reporting modules.
2.2 Out-of-Scope: Legacy system decommissioning, third-party vendor contracts, and post-go-live support SLAs.
2.3 Primary Objective: Deliver a production-ready framework that reduces implementation time by 30–50% compared to bespoke development.
Chief Data Officer (CDO) — Executive sponsor and primary decision-maker for platform adoption.
Model Risk Management (MRM) — Responsible for model validation, approval workflows, and ongoing monitoring.
Compliance & Legal — Ensures alignment with applicable regulations (SR 11-7, BCBS 239, IFRS 9).
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