Welcome back to the Risk Queue! There are major shifts reshaping banking's regulatory foundation this week as Moody's downgrades major U.S. banks following the sovereign rating cut, trade wars demand revolutionary "mesoeconomic" credit risk modeling, and plus more on AI.

-From Naeem, CEO & Founder - Risk On Q

In today's Risk Queue:

• Moody's strips government support assumptions from JPMorgan, Bank of America, and Wells Fargo

• Trade policy chaos forces banks to rethink credit models with "mesoeconomic" analysis

• GAO sounds alarm on AI amplifying lending bias across financial institutions

• Private credit's explosive growth creates $1 trillion shadow banking risk

• Michelle Bowman's Fed nomination signals era of "tailored regulation" and industry collaboration

• Deep dive: Model risk management evolution from traditional frameworks to AI-specific governance

Risk Headlines 

Risk Headlines

Moody’s downgrades JPMorgan, Bank of America, Wells Fargo in blow to U.S. banks - source yahoo.com

Moody's downgraded our major competitors JPMorgan, Bank of America, and Wells Fargo by one notch to Aa2, removing government support assumptions following the U.S. sovereign rating cut to Aa1. While this creates a rare blow to top-tier U.S. banks and may increase their borrowing costs, JPMorgan notably retained a positive outlook due to superior franchise strength and capital position.

Key Points:

• Systematic removal of sovereign backing assumptions for major banks

• Superior capital and franchise strength now more critical for ratings

• Higher borrowing expenses likely across downgraded institutions

• Evolving capital requirements amid political and fiscal pressures

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Trade Wars Should Motivate Banks to Rethink Credit Risk Management - source garp.org

The fundamental transformation centers on the inadequacy of traditional binary credit risk assessment in a world where tariff-driven supply chain disruptions create systematic correlations that transcend individual obligor analysis.

The current methodologies focusing exclusively on individual probability of default and broad macroeconomic scenarios cannot capture the nuanced impacts of trade policy on interconnected firm networks.

The emergence of "mesoeconomics" as a critical analytical layer reflects the need to understand how policy changes cascade through specific industry clusters and geographic regions.

Key Points:

• Methodological Evolution: Traditional credit models insufficient for capturing tariff-driven correlations

• Sectoral Bifurcation: Clear winners and losers emerge based on protection versus exposure

• Regulatory Capital Pressure: IFRS 9 forward-looking provisions demand immediate adjustments

• Strategic Differentiation: Superior analytical capabilities become competitive advantages

A.I. Risk / Technology Risk

AI Safety, Ethics, and Society Risks - source aisafetybook.com

The same AI capabilities that promise global benefit are also prone to catastrophic misuse due to competitive, military, and economic incentives. Whether driven by nation-state rivalry or corporate survival instincts, these pressures incentivize recklessness, cutbacks in safety, and a rush to deploy before understanding control mechanisms.

Underneath is a common thread of collective action failure: actors optimizing individually while compromising global stability. Governance at all levels—corporate, national, international—is fragmented or lagging behind.

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AI Use in Financial Services Could Add to Bias Risks - source routefifty.com

The GAO's analysis demonstrates that current risk management frameworks designed for traditional statistical models are inadequate for addressing AI's dynamic, complex, and often opaque decision-making processes. This represents a critical governance gap where financial institutions are deploying transformative technologies without commensurate risk controls, creating systemic fair lending exposure.

The regulatory response acknowledges this inadequacy through calls for AI-specific guidance and expanded oversight authority, signaling a fundamental shift in how algorithmic decision-making will be supervised and controlled.

Regulatory News - Fines, Losses, & Rules

Regulatory News - Fines, Losses, & Rules

Boston Federal Reserve: Could the Growth of Private Credit Pose a Risk to Financial System Stability?  - source bostonfed.org

The private credit market has exploded from $46 billion to $1 trillion since 2000, creating a competitive threat to traditional C&I lending while simultaneously exposing us to concentrated systemic risk through extensive credit line relationships with Private Credit funds.

Private credit growth represents sophisticated financial disintermediation where banks maintain systemic exposure to markets they no longer directly control, creating new forms of concentrated risk transmission.

Key Points:

The structural evolution suggests a fundamental shift in credit intermediation, with banks potentially becoming utilities providing liquidity infrastructure rather than primary credit originators.

• Disintermediation Evolution: Banks transitioning from direct lenders to liquidity providers for competitors

• Risk Concentration: Indirect exposure through credit lines creates systemic transmission channels

• Market Convergence: PC loan characteristics increasingly resemble traditional bank products

• Structural Protection: Senior secured positions provide downside protection but concentration risk

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Banks are Cheering Michelle Bowman’s Nomination as the Federal Reserve’s New Vice Chair for Supervision - source barrons.com

Michelle Bowman's nomination as Vice Chair for Supervision represents a fundamental shift toward lighter banking regulation, with her community banking background and "tailored regulation" philosophy promising significant relief from post-crisis regulatory burdens.

Her vocal opposition to blanket capital requirement increases and support for more transparent stress testing processes align directly with industry priorities, contrasting sharply with the tough-on-banks approach of her predecessor Michael Barr.

Key Points:

• Regulatory Recalibration: Shift from uniform standards toward risk-proportionate supervision

• Industry Collaboration: Move from adversarial to cooperative regulatory relationship

• Institutional Independence: Demonstrated willingness to challenge Fed consensus and leadership

Geek Out On Risk Data

Risk Management

Managing Model Risk: A Key Subset of Non-Financial Risk - riskonq.com

This week, we’re turning our attention to Model Risk. Last week, we dove into Compliance Risk, a Non-Financial Risk type. As we will see, the range of non-financial risks that banks must manage is even broader than their financial risks.

We will continue our focus on non-financial risk types to deepen our understanding and explore how they fit into the broader risk management ecosystem within the financial sector.

Model Risk Management: Comprehensive Analysis for Financial Institutions

What Is Model Risk?

Model Risk is the potential for adverse outcomes—financial loss, poor decisions, or reputational harm—arising from the use of a model that is flawed or misapplied. This includes errors in development, data, implementation, or use.

How Does It Differ From or Interact With Other Risks?

• Operational Risk: Misuse of models or system failures in model implementation.

• Compliance Risk: Regulatory breaches when models don’t meet SR 11‑7 or Basel requirements.

• Reputational Risk: Stakeholder distrust following model failures (e.g., in credit scoring or risk reporting).

• Strategic Risk: Erroneous model-driven decisions misalign strategic direction.

Why Is It Important for Financial Institutions Today?

• Models are central to credit underwriting, pricing, stress testing, AML, fraud detection, etc.

• Growing model complexity—especially AI/ML—heightens opacity and error potential

• Regulators expect structured, robust MRM frameworks—non-compliance can trigger enforcement actions (Fed, OCC, OSFI, AMF)

• As AI proliferates, new risks like bias, explainability, and data drift become front-and-center

Key Categories/Sources of Model Risk

1. Model Development Errors

   • Incorrect assumptions, flawed algorithms, logic bugs.

2. Data & Input Quality

   • GIGO: poor quality, bias, incomplete or outdated data.

3. Implementation & Use

   • Mismatches between model design and real-world deployment.

4. Validation & Model Change

   • Insufficient vetting, inadequate model re-validation after changes.

5. AI/ML‑Specific Risks

   • Opacity, dynamic retraining, bias, and fair use transparency

How It Appears Across Institutions & Products

Regulatory Environment

U.S.

• SR 11-7 (Fed & OCC) — Core expectations on model development, validation, governance, and controls

• Interagency AML/ML guidance emphasizes banks’ responsibility for third-party models used in compliance

International

• Basel Committee: Pillars on capital adequacy and operational risk include model risk.

• OSFI (Canada): Draft Guideline E‑23 outlines lifecycle governance from rationale to decommissioning .

• AMF (Quebec): Lifecycle policies, validation frequency, model inventory, documentation requirements

• EU (ECB/PRA): Aligning with SR 11-7 themes; impending EU AI Act for high-risk model deployment.

Risk Management Strategies

Traditional Approaches

• Governance via a Three Lines of Defense

• Policy frameworks with clear roles (identification, development, validation, use)

• Model inventory with risk rating, version tracking

• Independent model validation and documentation

• Control testing, audit reviews, and issue management

Modern & Tech-Enabled Practices

• AI/ML-Specific Governance: Explainability, retraining protocols, bias checks

• Automation & MRMaaS: Third-party platforms delivering end-to-end oversight

• Integrated risk systems that combine MRM into broader ERM platforms

• Real-time monitoring dashboards using metrics for drift, performance, anomalies

• RegTech & NLP tools to scan model changes and regulations automatically

Lifecycle & Governance Flow

Below is a standard model lifecycle flow with key components & stakeholders:

Emerging Trends

• AI Governance & Responsible AI Standards: Implementing ISO 42001; embedding NIST AI RMF and Trustworthy AI principles

• Generative AI in MRM: Generative models being used for validation, simulation; still early-stage

• MRM-as-a-Service (MRMaaS): Outsourcing validation and monitoring to experts

• Ethical & Explainable Modeling: Bias testing, documentation for decision transparency.

• Cloud + AI Integration: Scalable frameworks monitoring risk in SaaS platforms

• Climate & ESG Risk Modeling: Increasing pressure to model environmental risk scenarios.

Monitoring & Governance

Key Metrics & Indicators:

• Model performance deviations vs. benchmark

• Frequency of validation failures or exceptions

• KRI thresholds (drift, error rates)

• Time to remediate issues post-audit

Governance Structures:

• Three lines of defense: Business owners → Validator/Model Risk team → Internal Audit

• Reporting cadences: Board-level summaries, monthly risk committee dashboards

• Escalation protocols for model incidents or material misperformance

Actionable Insights & Common Pitfalls

✅ Success Factors

• “Compliance by design”: Embed MRM early in model lifecycle

• Strong tone from the top; clear governance and accountability

• Continuous regulatory surveillance and real-time tooling

• Cross-functional collaboration—risk, validation, tech, and business working together

⚠️ Common Pitfalls

• Treating model risk as a checkbox activity

• Ignoring model drift and outdated assumptions

• Weak documentation, especially around AI explainability

• Overlooking third-party model risk and vendor governance

• Insufficient model change management

Real-World Example

A mid‑sized U.S. bank leveraged AI‑enabled model risk dashboards to track retail credit scoring models across zip codes in near real‑time. Prompt insights into unexplained bias trends allowed proactive adjustments precluding regulatory breaches and enhancing credit accuracy—strengthening both validation and business outcomes.

📌 Bottom Line

Model risk is now a central, complex aspect of managing financial risk management. Institutions with proactive MRM frameworks—blending robust governance and advanced AI tools—can transform model risk from a regulatory burden into strategic advantage.

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Thank you for reading.

Naeem

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