Fintech AI Compliance
Fintech is the most heavily model-risk-managed industry on earth. The AI Act adds a layer. Here is how to keep SR 11-7, fair lending, and AI Act regimes coherent without running three parallel programs.
Financial services have governed models for decades. The AI Act is not asking the industry to invent something new. It is asking the industry to extend an existing discipline to systems that move faster than the discipline was designed for.
The starting point is model risk management. SR 11-7 in the US, and analogous frameworks globally, have set the bar for how predictive models get developed, validated, and governed inside regulated financial institutions for over a decade. The three-pillar approach (development, implementation, and use; effective challenge through validation; governance) maps cleanly onto AI systems. The adaptations are real but bounded: the framework holds. Most large banks have already extended their MRM scope to cover AI by name.
Fair lending is the regime that produces the most legal exposure. An AI credit model can produce decisions that systematically disadvantage protected classes without anyone intending discrimination, and the lender is still liable. The defence is a combination of pre-deployment fairness testing, runtime monitoring for disparate impact, and an audit trail that lets the bank demonstrate the basis for any individual adverse action when a customer or regulator asks.
What the AI Act adds
For European fintech, the EU AI Act explicitly names credit scoring of natural persons as a high-risk AI system. The high-risk obligations layer on top of existing financial regulation rather than replacing it. Practically, the same model now needs an MRM file, a fair-lending analysis, and an AI Act technical file, with substantial overlap. The teams that design for the union spend roughly half what the teams running these as separate workstreams spend.
Where Navedas fits
Navedas attaches the audit trail every regime separately requires, in one runtime. Each model decision is logged with inputs, the rule that fired, the citation, the verdict, the policy version, and any fairness analysis that applied. The MRM file, the fair-lending defence, and the AI Act documentation all draw from the same source.
Articles & resources
Audit-Ready Compliance
One audit trail across MRM, fair lending, and AI Act documentation.
Explore → SolutionAI Risk Containment
The runtime decision layer that catches disparate impact and policy violations at decision time.
Explore → ResourceEU AI Act Readiness
Map credit-scoring and creditworthiness systems against the high-risk provisions.
Explore → ToolQuarterly Exposure Calculator
Quantify fair-lending and disparate-impact exposure across your AI decision surface.
Calculate →Frequently asked questions
What regulations apply to AI in fintech?
The starting point is model risk management (SR 11-7 in the US, similar frameworks globally), which has governed predictive models in finance for years. Layered on top: fair lending laws (ECOA, Reg B, FCRA, the UK Equality Act, etc.), AML and sanctions, consumer protection rules around explanations and adverse action notices, and now the EU AI Act high-risk category for credit-scoring and similar systems.
How does SR 11-7 apply to AI?
SR 11-7 is regulator guidance on model risk management at US banks, and it applies to any model that drives material decisions, including AI. The three-pillar approach (development, implementation, use; validation; governance) maps cleanly onto AI systems, with adaptations for the dynamic and emergent nature of modern models. Most large banks have already extended their MRM frameworks to cover AI.
What is the fair lending exposure for AI?
Disparate impact. An AI credit model can produce decisions that systematically disadvantage protected classes even without intentional discrimination, and the lender remains liable. The mitigation is a combination of pre-deployment fairness testing, runtime monitoring, and the kind of audit trail that lets you demonstrate the basis for any individual adverse action.
Where does the EU AI Act fit for European fintech?
Credit scoring of natural persons is named explicitly as a high-risk AI system under the AI Act. So is AI used to evaluate creditworthiness. The high-risk obligations layer on top of existing financial regulation rather than replacing it, which means European fintech needs to satisfy the union of model risk, fair lending, and AI Act requirements simultaneously.
Related topics
One audit trail across every fintech regime.
See how the realtime decision layer satisfies MRM, fair lending, and AI Act requirements with a single operational discipline.