As AI and machine learning models become embedded in critical financial decisions, regulatory expectations are accelerating. We bring deep quantitative expertise and hands-on MRM experience to help institutions deploy AI responsibly — with governance frameworks, independent validation, and bias testing that stand up to examination.
Request a ConsultationFinancial regulators are increasingly focused on AI and machine learning models. SR 11-7 applies to AI/ML models just as it does to traditional quantitative models — but AI introduces unique challenges around explainability, bias, data drift, and model opacity that existing governance frameworks weren't built to address.
Institutions deploying AI models without adapted governance frameworks face heightened regulatory risk, fair lending exposure, and potential MRAs that are significantly more complex to remediate after the fact.
AI/ML models fall squarely within SR 11-7 scope. Examiners expect the same rigor in conceptual soundness, outcomes analysis, and ongoing monitoring — with additional attention to model interpretability.
OCC guidance emphasizes sound model risk management for AI-driven decision systems, including credit underwriting, fraud detection, and BSA/AML compliance.
CFPB has made clear that black-box AI does not exempt institutions from adverse action notice requirements and fair lending obligations.
Federal agencies are actively developing AI-specific guidance. Institutions that build governance frameworks now will be ahead of compliance requirements, not behind them.
Our team has hands-on experience validating AI and machine learning models across the full spectrum of financial applications — from traditional supervised learning to large language models and generative AI.
Vendor model expertise: We have deep experience validating AI/ML-based vendor solutions including NICE Actimize, Verafin, and other platforms where the underlying algorithms are proprietary and require specialized validation approaches.
Independent validation of AI/ML models covering conceptual soundness, data integrity, performance testing, sensitivity analysis, and outcomes assessment — adapted for the unique characteristics of machine learning models.
Systematic testing for disparate impact and algorithmic bias across protected classes. We quantify fairness metrics, identify sources of bias in training data and model architecture, and recommend remediation strategies.
End-to-end governance policies and procedures for AI/ML models — covering model inventory classification, risk tiering, approval workflows, and ongoing monitoring programs proportionate to your institution's size and AI footprint.
Assessment and implementation of model explainability techniques — SHAP values, LIME, feature importance analysis — to meet regulatory expectations and support adverse action notice requirements.
Specialized risk evaluation for large language model deployments including hallucination risk, prompt injection vulnerabilities, data privacy exposure, and output quality governance.
Design and implementation of AI-specific monitoring programs that detect data drift, concept drift, performance degradation, and emerging bias — before they become regulatory findings.
We apply the same disciplined validation framework that regulators expect under SR 11-7, adapted for the unique characteristics of AI/ML models.
We assess the AI model's intended use, materiality, complexity, and regulatory exposure. Models are risk-tiered using a methodology that accounts for AI-specific risk factors including opacity, data dependency, and decision impact.
AI models are only as good as their training data. We evaluate data quality, representativeness, potential for embedded bias, feature engineering decisions, and data lineage from source to model input.
We evaluate the appropriateness of the model architecture, algorithm selection, hyperparameter tuning, and design decisions against the intended use case and available alternatives.
Rigorous out-of-sample testing, benchmarking against alternative approaches, stability analysis, and comprehensive fairness testing across protected classes using industry-standard metrics.
We evaluate whether the model's decisions can be adequately explained to stakeholders, regulators, and — where required — to consumers. We recommend and implement appropriate interpretability techniques.
Exam-ready validation reports with clear findings, risk ratings, and actionable remediation paths. We stay engaged through issue resolution and ongoing monitoring implementation.
Our team includes practitioners who have built and governed AI/ML programs at major financial institutions, published research on bias detection methodologies, and led workshops on machine learning model fairness. We combine deep quantitative expertise with practical regulatory experience — we don't just understand the math, we understand what examiners look for.
Advanced degrees from UCLA, Sorbonne, MIT, and Tulane. We understand the mathematics behind AI/ML at a foundational level.
Team members who have held senior MRM roles at the Federal Reserve, Wells Fargo, Bank of America, and Lloyds Banking Group.
Active contributors to the field, including workshops on detecting and correcting bias in machine learning models.
How ML models used in automated decision-making systems can harbor and perpetuate bias — and what to do about it.
Hands-on workshops covering fairness metrics, bias detection using Python, and correction methods for machine learning models.
Whether you need a single AI model validated or a comprehensive governance framework, our team is ready to help.