SR 11-7
Aligned validation framework
Complete
Model inventory delivered
ECOA
Adverse action explainability
Audit-ready
Sponsor bank MRM package
Client Snapshot
Industry
FinTech
Solution
AI, GenAI & ML | IT Consulting
Complexity
Medium-High
Delivery
Advisory + Program Design
The Problem
FinTech platforms using AI for credit decisioning, fraud detection, pricing, or underwriting operate under model risk management expectations equivalent to regulated banks whether as a direct regulatory requirement or as a condition of their sponsor bank program agreement. Most FinTechs lack the model validation capability that SR 11-7 expects: a model inventory that includes all production models, documented validation methodology for consequential models, and ongoing performance monitoring with defined response protocols.
The governance gap widens with scale. The first credit model was built by a data scientist without documentation. The fraud model was updated three times without version control. The pricing model uses a feature that correlates with a protected class characteristic and no fair lending analysis has been conducted. At Series B, these gaps are visible to sophisticated investors. At the first regulatory examination or sponsor bank audit, they are findings with program agreement consequences.
Ready to Start?
Schedule an AI Model Governance Assessment
Get a candid analysis of your current model inventory, documentation gaps, and sponsor bank audit readiness.
3x
higher likelihood of undocumented AI models at FinTechs that built first models before establishing governance, per advisory firm benchmarking. Retroactive documentation of undocumented production models is significantly more expensive than prospective governance built into the model development process. The cost of governance increases with every quarter it is deferred.
How PiTech Delivers
01
Model Inventory and Risk Classification
Complete AI/ML model inventory across all production and development environments including models built by data scientists, models embedded in third-party tools, and models inherited through acquisitions or partnerships. Risk classification per model aligned to SR 11-7 tiers and CFPB fair lending expectations for consumer-facing models.
02
Validation Framework and Playbooks
Model validation playbooks for FinTech AI use cases: credit underwriting, fraud detection, pricing optimization, and customer behavior models. Behavioral testing, adversarial scenario analysis, and performance monitoring specifications for each model category. Validation methodology documented at a level suitable for sponsor bank audit review.
03
ECOA and Fair Lending Integration
Proxy variable analysis and disparate impact testing integrated into the development lifecycle for all consumer-facing models. ECOA Regulation B adverse action reason code generation for credit decision models producing top reason codes at the individual applicant level in compliant format.
04
Sponsor Bank Audit Package
MRM documentation library organized for sponsor bank program audit: complete model inventory with risk classification, validation results for consequential models, ongoing monitoring dashboards, and incident log. Maintained current between audit cycles through the lightweight model intake process not assembled reactively before each audit.
Proven Outcomes
Complete
Model inventory delivered including undocumented and third-party embedded models
SR 11-7
Aligned validation documentation accepted in sponsor bank program reviews
18+ yrs
Financial services AI experience SR 11-7, CFPB, and fair lending depth
Proven Outcomes
18+
Years in Regulated Industries
What You Gain
Complete
Model inventory with risk classification for all production AI models
SR 11-7
Aligned validation documentation for all consequential models
Fair lending
Testing integrated into development lifecycle for consumer-facing models
Audit-ready
Sponsor bank MRM documentation maintained current between cycles
What's Included
Model inventory methodology
Model inventory methodology
Risk classification framework
Risk classification framework
Validation playbooks
Validation playbooks
Fair lending testing protocol
Fair lending testing protocol
ECOA adverse action explainability
ECOA adverse action explainability
Ongoing monitoring framework
Ongoing monitoring framework
Sponsor bank MRM audit package
Sponsor bank MRM audit package
Frequently Asked Questions
Does SR 11-7 directly apply to FinTechs?
SR 11-7 is technically a Federal Reserve supervisory letter for banks. However, sponsor bank program agreements typically require FinTech partners to meet equivalent model risk management standards for models that influence credit decisions, fraud determinations, or other consequential consumer outcomes. CFPB examination guidance also establishes model risk expectations for supervised nonbank entities. In practice, FinTechs using AI for credit or fraud face functional SR 11-7 equivalence through their sponsor bank relationship.
What is the minimum governance required for a FinTech credit underwriting model?
For a credit underwriting model at a FinTech operating through a sponsor bank, the minimum governance includes: documented methodology, validation on a holdout dataset with performance metrics, fair lending analysis with proxy variable testing, ECOA adverse action reason code capability, ongoing performance monitoring specifications, and a defined retraining process. PiTech delivers each of these as defined engagement deliverables not as aspirational documentation.
How does PiTech handle models that were built without documentation?
PiTech conducts retroactive model documentation for undocumented production models: interviewing model owners, reconstructing methodology from code and training artifacts, conducting validation on available data, and producing documentation that satisfies current governance standards. Retroactive documentation is more expensive than prospective governance but consistently preferable to operating undocumented models during sponsor bank audit or regulatory examination.
How does the model intake process prevent governance debt from accumulating?
PiTech designs a lightweight model intake checklist that each new model completes before production deployment: a standardized development documentation template, a risk classification determination, a validation requirement based on the classification, and model inventory registration. The process adds minimal development friction approximately 2–4 days per model while preventing the governance debt accumulation that creates expensive retroactive remediation.
What does PiTech deliver specifically for ECOA adverse action compliance?
PiTech implements adverse action reason code generation as part of the model architecture: an explainability layer that produces the top contributing features to an adverse credit decision in plain-language format suitable for ECOA Regulation B adverse action notices. The reason codes are generated at the individual applicant level not as generic model-level descriptions satisfying the individual-level explainability requirement of Regulation B.
AI model governance is the compliance frontier FinTechs are navigating now. PiTech builds frameworks that satisfy sponsor banks and prepare for direct regulatory examination.
Contact PiTech to begin with an AI model inventory and governance gap assessment.
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Contact Details
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