Model Risk Management (MRM)
The Rules of Model Risk Management Just Changed. Is Your Framework Ready?
On April 17, 2026, the Federal Reserve, FDIC, and OCC replaced SR 11-7 and all prior MRM guidance with a new, risk-based, principles-driven framework for model risk management. Every financial institution with significant model use now needs to assess whether their existing MRM program meets the new standard. Data Geny helps banks, fintech companies, and financial institutions build MRM frameworks aligned to the 2026 revised guidance covering traditional models, machine learning, and generative AI.
Why Model Risk Is Now One of the Highest-Priority Issues in Financial Services
Financial institutions have always relied on models for credit scoring, capital calculations, stress testing, and pricing. But the model landscape has fundamentally changed. Use of models within banking and financial services continues to grow in complexity and scope, with advances in technology and increased competition driving organizations to leverage innovative approaches to improve efficiencies, better mitigate risks, and maximize profits.
AI and machine learning models are now embedded in fraud detection, underwriting, customer analytics, and AML monitoring. Generative AI is being deployed in customer-facing and internal workflows. Each of these introduces model risk that traditional MRM frameworks were never designed to handle.
MRM Gap Assessment & Current-State Review
The Problem: Most financial institutions have some form of MRM framework but few have assessed whether it covers their full model inventory, including newer ML and AI models, or whether it aligns with the 2026 revised guidance.
What We Do: We conduct a structured assessment of your current MRM program against the 2026 interagency guidance evaluating your model inventory, tiering methodology, validation processes, monitoring practices, and governance structures. We identify gaps, prioritize remediation by risk level, and give you a clear picture of your regulatory exposure.
Output:
- Current-state MRM assessment report
- Gap analysis mapped to 2026 revised guidance requirements
- Model inventory review and tiering recommendations
- Risk-ranked remediation roadmap
Model Inventory & Risk-Based Tiering
The Problem: You can’t manage model risk you haven’t catalogued. Many organizations have fragmented model inventories missing newer AI tools, shadow models, or vendor models which creates blind spots in governance and regulatory exposure that is difficult to defend.
What We Do: We help you build and maintain a complete, structured model inventory that covers all model types statistical, machine learning, generative AI, vendor-supplied, and third-party. We then design a risk-based tiering framework that classifies each model by inherent risk, business purpose, and regulatory exposure ensuring Tier-1 material models carry full lifecycle oversight while lower tiers receive proportionate, evidenced controls.
Output:
- Complete model inventory design and population
- Risk-based model tiering framework
- Tiering evidence and documentation for examiner review
- Ongoing inventory maintenance process
Our Approach to Model Risk Management (MRM)
We deliver enterprise MRM programs through a structured methodology:
Assessment & Inventory: Identify and evaluate all models and risk exposure
Governance Framework: Define policies, roles, and oversight processes
Validation & Testing: Conduct rigorous testing, backtesting, and stress scenarios
Monitoring & Recalibration: Track performance and adjust models proactively
Compliance & Reporting: Ensure audit readiness and regulatory adherence
Integration with AI & Analytics: Apply MRM across predictive and ML models
Why Choose datageny.com
Deep expertise in financial model risk management and regulatory compliance
Proven experience validating enterprise-scale models, including AI and predictive models
Strong focus on governance, auditability, and operational reliability
End-to-end MRM framework design, implementation, and monitoring support
Integration with analytics pipelines and enterprise decision-making systems
Model Development Standards & Governance
The Problem: Model risk often originates during development in undocumented assumptions, poorly understood data inputs, or inadequate testing. Without clear development standards, MRM teams spend their time firefighting validation issues rather than providing meaningful oversight.
What We Do: We design model development governance standards that define requirements for documentation, data quality, assumption testing, and technical review applied at the start of the model lifecycle, not retrospectively. The 2026 guidance requires banks to “shift left,” moving risk controls to the very start of the model lifecycle our frameworks are built around this principle, embedding governance into development workflows rather than adding it as an afterthought.
Output:
- Model development standards documentation
- Development-stage governance checklist by model tier
- Documentation templates for assumptions, data inputs, and design decisions
- Pre-validation quality gates
Independent Model Validation
The Problem: Independent validation is the cornerstone of any MRM framework but many institutions struggle to execute it consistently, especially for machine learning models where traditional validation techniques don’t fully apply.
What We Do: We support financial institutions in designing and executing independent model validation processes across all model types. For traditional statistical models, this covers conceptual soundness, outcome analysis, and sensitivity testing. For ML and AI models, we extend validation to include feature importance analysis, distributional testing, bias assessment, explainability evaluation, and stress testing under adverse conditions.
Effective challenge requires challenger models, outcomes analysis, benchmarking, and sensitivity testing to be versioned and reproducible not a one-time memo. Our validation frameworks are built to that standard.
AI & Generative AI Model Risk
The Problem: Generative AI models introduce model risk that existing SR 11-7 frameworks were never designed to address requiring incremental testing across conceptual soundness, outcome analysis, and ongoing monitoring that goes beyond traditional MRM practice. Most institutions are deploying generative AI faster than their MRM programs can keep pace.
What We Do: We design MRM frameworks specifically extended for generative AI and large language models covering model selection and scoping documentation, prompt governance, output validation, hallucination risk assessment, and ongoing monitoring for performance drift and misuse. Every element is aligned to the 2026 revised guidance and emerging AI regulatory expectations including the EU AI Act.
Output:
- Generative AI model risk policy and classification framework
- Prompt governance standards
- GenAI-specific validation and testing methodology
- Ongoing monitoring design for AI model outputs
Model Monitoring & Ongoing Oversight
The Problem: A model that passed validation at launch may be silently underperforming today due to data drift, changing customer behavior, or market conditions that have shifted since the model was built. Without continuous monitoring, organizations discover model failures in regulatory submissions, not in monitoring dashboards.
What We Do: We design ongoing model monitoring frameworks that track performance stability, data drift, concept drift, and output distribution across your model portfolio with automated alerting when thresholds are breached and governance workflows that determine when revalidation or retirement is required.
The 2026 guidance requires continuous monitoring not periodic snapshots with performance tracking, stability assessment, and full lineage maintained across the model lifecycle. Our monitoring frameworks are built to satisfy this requirement.
MRM Governance, Policy & Organizational Design
The Problem: Effective MRM requires more than technical processes — it requires a governance structure with clear accountability, escalation paths, and board-level visibility into model risk. Many institutions have MRM processes but lack the organizational design to make them function consistently.
What We Do: We help financial institutions design the governance structures that make MRM work as an enterprise capability — including model risk policy documentation, governance committee design, three-lines-of-defense accountability mapping, and escalation frameworks. We also design the reporting structures that give senior management and boards meaningful visibility into model risk exposure across the portfolio.
Output:
- Model risk policy and standards documentation
- MRM governance committee structure and terms of reference
- Three-lines-of-defense accountability framework
- Executive and board model risk reporting design