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End-to-end Enterprise AI Model Governance

Continuous compliance of business, operational, regulatory and risk guidelines
What models are complying with AI regulations?
How serious are the compliance breaches?
Do I have the information for my next audit?
With end-to-end AI model governance, you always know.
How can you shorten the time for model validation?
What were the results of the validated models?
Are models performing within risk controls?”

AI Model Governance starts at the beginning

AI model governance starts at the time of ideation and use case definition and ends at model retirement. Successful AI model governance requires a set of standards and processes that are adhered to throughout the model life cycle – development, validation, productionization, and operations to retirement.
How we do it:
  • A set of predefined automated processes make it fast and easy to define and establish business, risk, and compliance rules for the entire model life cycle.
  • A single model inventory for all AI and analytic models, regardless of type or where they are run, give you the control to apply standards and rules across all models in the enterprise.
  • A powerful automated documentation service creates breach reports, model design documentation and other custom compliance and risk documents and reports that are essential for model risk management and governance.
AI Regulations
  • U.S. SR 11-7. Guidance on Model Risk Management
  • EU AI Act
  • FINRA AI in the Securities Industry
  • UK ICO AI Auditing Framework
  • H.R.2231 – Algorithmic
  • Accountability Act of 2019
  • Canada Directive on Automated Decision Making
  • Singapore Model AI Governance Framework

Automated, Reflexive Monitoring for business, risk, and compliance accuracy

Models are continuously learning, which often leads to changes in model decisioning. Operational models must be constantly checked for accuracy. If changes in decisioning or operational performance are not detected and immediately resolved, unreliable decisioning can put the business at risk. Equally important is understanding what changed and why.
How we do it:
  • Reflexive monitoring that includes continuous observation, real-time problem detection and immediate remediation keeps business goals on track.
  • Built-in explainability, traceability and auditability provide the transparency and accountability for business improvement and successful audits.

Visibility and reporting – so you always know

Complying with AI regulatory and business guidelines means knowing you did and proving it. Visibility into the business and compliance state of each and every model provides data science, IT, model risk management, and compliance teams the insights to ensure models remain compliant and understand where to make adjustments or fill gaps when they are not.
How we do it:
  • Compliance, audit, and regulatory reporting differs for every company and potentially every audit.
  • The comprehensive and persistent data retained on each model throughout its life delivers the reproducibility that is often critical for passing audits and adhering to AI regulatory guidelines.
  • Integrations with Tableau and Power BI give you the flexibility of custom reporting so you are ready and able to pass any audit at any time.

See how ModelOp Center can govern and scale your AI initiatives.

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