Preparing Model Risk Management for AI Governance

AI Governance is Moving From Theory to Practice

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In our Q3 Solytics Partners MRM Global webinar, “Preparing MRM for AI/ML and Generative AI”, we asked financial institutions how they are approaching the rise of AI systems and AI/ML models. The poll results show a sector that is actively building frameworks to manage risk, bias, and rapid adoption of AI.

Industry Insights: A Snapshot of Industry Readiness

The poll of banking and financial services professionals revealed five core insights:

Automation is in Demand – 88.6% want automated workflows for model inventory, documentation, and scoring.
GenAI Controls are in Motion – 80.3% are developing frameworks specifically for generative models.
Bias and Explainability Persist – 75.4%have encountered issues with transparency or fairness.
AI/ML Validation is Mainstream – 74.7% already validate or oversee AI/ML models as part of their MRM programs.
Adoption Timelines are Short – Nearly half (47.5%) plan to roll out GenAI use cases in the next three months; 80% within a year.

One clear message: Financial institutions are preparing fast, but the risks are evolving just as quickly.

Why Does It Matter?

Model Validation is Expanding: Model validation used to focus on statistical models for credit, market, or operational risk. Now, with AI/ML embedded in fraud detection, underwriting, and customer analytics, model validators are on the front line of AI governance when used in the context of modeling. Around 74.7% of respondents already validate AI/ML, which shows that governance is no longer optional, and it has become part of daily risk management.
Generative AI Brings New Risks: Unlike traditional models, Gen AI systems (e.g., large language models) can produce inaccurate results, leak sensitive data, or introduce bias in unexpected ways. With 80.3% of institutions preparing dedicated GenAI frameworks, it's clear firms see the need for policies to be tailored to these risks and not just extensions of existing guidelines. While MRM may not be involved in the governance of certain use cases (those prone to outside models), it still provides valuable insights into it.
Explainability and Fairness Are Still Unsolved: Three out of four respondents struggle with explainability or bias. The challenge is building technical solutions (like SHAP, LIME, and fairness metrics) that translate into practical governance. Hence, more innovative and RAG approaches are emerging to validate Gen AI models. Regulators are watching closely
- The European Banking Authority (EBA) has stressed the importance of explainability in AI-driven decision-making.
- In the U.S., OCC and Federal Reserve SR 11-7 remain reference points for model validation, requiring transparency in risk management.
Automation is Essential for Scale: With 88.6% seeking workflow automation, manual governance is no longer sustainable. Institutions need end-to-end Inventory and Governance platforms that can centralize model inventory, automate documentation, and embed risk scoring. This shift mirrors broader trends in financial institutions toward RegTech and digital-first oversight.
The Clock is Ticking: With 47.5% deploying GenAI use cases within three months, that means governance cannot lag. Firms that fail to establish frameworks early may face regulatory or reputational fallout when issues arise.

‍Industry Insights: A Snapshot of Industry Readiness

The poll results point to a clear roadmap for financial institutions as they strengthen their MRM programs for AI and Generative AI:

‍Immediate (0–3 months)
- Create a complete inventory of AI/ML and GenAI models to establish a comprehensive view of the institution’s models.
- Form a cross-functional AI governance committee bringing together Risk, Compliance, IT, and Data Science to ensure alignment.
- Should adopt a “safe-to-deploy” checklist covering privacy, security, and ethics for every deployment decision.
Mid-Term (3–12 months)
- As adoption expands, financial institutions should automate governance workflows from model registry and risk scoring to documentation to reduce operational burden and improve consistency.
- Financial institutions need to deploy explainability and bias detection toolkits, ensuring the outputs are fair and transparent.
- Model Validators must be trained to train validators on GenAI-specific risk profiles, equipping them with the skills to evaluate risks that go beyond traditional statistical models.
Long-Term (12+ months)
- Institutions should develop integrated AI governance dashboards that enable real-time monitoring and reporting of model risks.
- Governance practices must also be embedded directly into CI/CD pipelines, ensuring that controls evolve in step with continuous model development and deployment.
- To demonstrate accountability, firms should conduct independent third-party audits and benchmarking, providing external validation of their governance maturity.

By taking these steps, financial institutions can move beyond compliance to build resilient, transparent, and future-ready AI governance frameworks that protect trust while unlocking innovation.

Solytics Partners' Trusted Model Risk Ecosystem:

At Solytics Partners, we empower financial institutions with advanced MRM automation, governance, and AI-powered analytics to streamline compliance and enhance risk management frameworks.

Nimbus Uno - Advanced Risk Analytics Platform that enables AI/ML-driven risk modeling and analytics. (https://www.solytics-partners.com/products/nimbus-uno)
MRM Vault - Robust Model Governance Platform designed to streamline model lifecycle management and compliance. (https://www.solytics-partners.com/products/mrm-vault)
MoDeVa - A Powerful Modeling Library featuring ML modeling frameworks, interpretable AI/ML models, fairness, and debiasing techniques. (https://www.solytics-partners.com/products/modeva)