Model Risk Management (MRM) Validator

Principal workflows

• Inventory: Every model and model-like tool is recorded with its owner, purpose, use, data, risk tier and validation status, so the institution knows what it relies on and where the gaps are.
• Validate: Validators plan the validation, run standard tests against the model's development and outcome data, and record their own analysis and challenge alongside the results.
• Monitor: Between validations, scheduled monitoring compares the model's current inputs, scores and outcomes with its development baseline, and flags drift and bias for review.
• Remediate and approve: Findings are tracked to closure with the owner's response and evidence. Validation conclusions and model approvals go through the governance steps your policy sets.

Who uses it

• Model risk management teams at banks, credit unions and lenders
• Model validators who provide effective challenge
• Model owners and developers of credit scoring, fraud, AML, CECL, stress testing and AI models
• Fair lending and compliance teams who test models for disparate impact
• Model risk committees, internal audit and examiners

Reporting

• Model inventory by tier, owner, use and validation status
• Validations due, in progress and overdue
• Open findings by severity, owner and age
• Drift and performance monitoring results against thresholds
• Fair lending test results by model and period
• Program summary for the model risk committee and board

Features

• Model inventory: One inventory of models and model-like tools, with owners, uses, dependencies, risk tier and validation status, including vendor models.
• Risk tiering: Materiality and complexity questions propose a tier, which model risk management confirms or changes with a reason.
• Test library: Standard tests for discrimination, calibration, stability, sensitivity, back-testing and benchmarking, each documented with its method and interpretation.
• Reproducible test runs: Each run records the data snapshot, model version, code version and parameters, so it can be repeated and gives the same result.
• Drift monitoring: Population stability, characteristic stability and performance measures tracked against each model's baseline, with thresholds you set.
• Bias and fair lending analysis: Outcome and score comparisons across groups, using the methods and proxy approaches your fair lending program defines, with results kept for review.
• Explainability reports: Variable importance and reason-code analysis for complex and machine learning models, to support conceptual soundness review and adverse action reasons.
• Findings and remediation: Findings with severity, owner, due date and action plan, tracked to closure with evidence and validator sign-off.
• Validation report builder: A validation report assembled from the plan, tests, analysis and findings, which the validator edits and finalizes.
• AI-assisted review: Suggested gaps in model documentation and draft summaries of test results for the validator to check; validators make every conclusion.
• Governance workflow: Approvals for tiers, validation conclusions, conditions on use and exceptions, following your policy's levels and committees.
• Audit trail: Every inventory change, test run, finding, approval and export recorded with the person and time.

Integrations

• Data warehouse and data lake: Development, scoring and outcome data read from your warehouse or lake by scheduled query or file, mapped during implementation.
• Model development environments: Test runs can call your own Python or R code, and model artifacts and versions can be read from your model registry.
• Code repositories: The code version behind each model and test run linked from your Git repository.
• Moraph products: Scenario and model documentation shared with the AML & SAR Automation Hub, the IFRS 9 / CECL Impairment Calculator and Stress Testing & Capital Planning (DFAST).
• GRC and issue management: Findings and their status shared with your governance, risk and compliance system so issues are tracked in one place.
• Single sign-on: Staff sign in through your identity provider (SAML or OpenID Connect).
• Work API: Inventory, validation and monitoring data available to your data warehouse and reporting tools through the Moraph Work API.

Deployment

• Moraph cloud: Hosted and operated by Moraph in the United States region. United Kingdom and Australia regions are available for institutions outside the US.
• Dedicated environment: A single-tenant environment for institutions whose policies require isolation, scoped in the proposal.
• Tests run near your data: Where customer-level data must stay in your environment, test runs can execute there and return only results to the Validator, scoped in the proposal.

Security and privacy

• Single sign-on: Staff sign in through your identity provider, so your password and multi-factor policies apply.
• Role-based access: What each person can see and do follows their role; validators are independent of the models they validate, and owners cannot approve their own models.
• Restricted test data: Access to customer-level data and protected-class analysis is limited to the roles you choose; most users see aggregate results only.
• Encryption and keys: Client data is encrypted, with keys Moraph staff cannot use without explicit, time-bound authorization.
• Complete audit trail: Every view, change, test run, decision and export is logged with the person and time, and cannot be edited.
• Data residency: Data stays in the hosting region you choose: the United States, the United Kingdom or Australia.

Limitations

• The Validator does not validate models by itself. It runs tests, organizes evidence and suggests gaps; your validators provide the effective challenge and reach every conclusion.
• AI-generated summaries and gap suggestions are drafts for a validator to check. They are not findings until a person records them.
• Test results depend on the data you provide. Incomplete outcome data or a poorly defined baseline will limit what monitoring can show.
• Bias testing uses the groups, proxy methods and measures your fair lending program defines. The Validator does not determine whether a model complies with fair lending law; that is your institution's judgment, with counsel where needed.
• It is not a model development platform. Models are built and deployed in your own environments.
• Vendor models can be inventoried and monitored on their outputs, but their internal logic can be reviewed only as far as the vendor's documentation allows.
• It does not replace your model risk policy or committee. Tiers, thresholds and conditions on use are your institution's decisions, recorded in the Validator.

Next steps

Book a demonstration or a technical briefing at www.moraph.com/solutions/model-risk-management-mrm-validator. Purchasing routes and company information: www.moraph.com/how-to-buy.