From credit decisioning to GenAI copilots and AI agents, your models are in scope for model risk, fair lending and operational resilience rules. Asenion turns those requirements into operational controls your first, second and third lines can verify, monitor and evidence.
Schedule a Call30 minutes with our AI compliance team. Bring your model inventory questions.
Few of these were written only for AI. All of them apply to it.
Interagency model risk management guidance from the Federal Reserve, OCC and FDIC (April 2026), replacing SR 11-7: sound development, independent validation with effective challenge, and governance for every model.
For AI: ML scorecards, LLMs and vendor models are models. Expect questions on inventory, tiering, conceptual soundness, outcomes analysis and ongoing monitoring, GenAI included.
OSFI's model risk management guideline for every federally regulated financial institution, effective May 1, 2027.
For AI: Explicitly covers AI/ML. Requires an enterprise model inventory, risk ratings that drive governance intensity, and lifecycle controls from design to decommission.
Prohibits credit discrimination and requires specific, accurate principal reasons on adverse action notices.
For AI: Complex models get no exemption. You must explain specific denial reasons and test for disparate impact and less discriminatory alternatives.
Credit scoring and creditworthiness assessment of individuals are high-risk uses. Under the AI Omnibus, those obligations apply from December 2027; transparency duties have applied since August 2026.
For AI: Risk management, data governance, technical documentation, logging, human oversight and robustness controls for credit AI, plus disclosure when customers interact with AI.
The Digital Operational Resilience Act covers ICT risk management, incident reporting, resilience testing and ICT third-party risk for EU financial entities.
For AI: LLM and AI platform providers are ICT third parties. They belong in your register of information, contracts, exit plans and resilience testing.
The AI use cases we see most often, and the requirements that follow them.
ML and alternative-data models must be validated, explainable enough to produce adverse action reasons, and tested for disparate impact before and after release.
Models that set alert thresholds or suppress alerts need documented rationale, validation and drift monitoring. A missed suspicious activity report is a model risk finding.
Chatbots and copilots must disclose AI use, avoid hallucinated rates or product terms, protect customer data and resist prompt injection.
Agents acting in banking systems need runtime guardrails, least-privilege access and tamper-resistant logs. AI-generated code needs secure SDLC controls.
One set of controls, applied from model development through runtime, with evidence your validators, internal audit and examiners can rely on.
Start from Policy Packs for SR 26-2, OSFI E-23, ECOA and the EU AI Act, combined with your own model risk policy, as control objectives your MRM and compliance teams already recognize.
Test credit, fraud and GenAI models for bias, explainability gaps, hallucination, data leakage and prompt injection, with every result mapped back to a control.
Apply context-aware controls to AI agents in production and capture tamper-resistant evidence of what happened, which controls applied and whether they worked.
In 30 minutes we'll map your AI use cases to the rules above, show the controls that apply, and point out where your model risk program has gaps.
Schedule a CallNo preparation needed.
