Sectors / Financial services
Model risk and explainability, with lineage back to every input.
Financial institutions already run model risk management. aerais connects it to the data underneath, so a model decision can be traced to its inputs.
Why it matters
Why governing data and AI together matters in financial services
Banks, lenders and asset managers have long managed models under established risk disciplines. AI widens the surface: more models, more vendors, more unstructured inputs, and decisions that reach customers faster than review cycles.
The hard question is rarely the model alone. It is whether the team can show which data fed a decision, who approved it, and what changed since. Without lineage back to every input, explainability stays a narrative rather than evidence.
Typical questions
Five questions we hear in this sector
- Can we show, for a given decision, which source systems and transformations fed the model?
- Which models and vendor tools in use are not yet in the model inventory?
- Who approved this data for use in this model, and when did that approval last change?
- If an upstream feed is wrong, which downstream models and reports are affected?
- Can one set of control evidence serve model risk, privacy and AI regimes at once?
Chain of custody
How the chain applies
Data plane controls here are lineage and metadata, classification of customer and trading data, and quality gates on features used by credit, fraud and risk models.
These seam loops tend to matter most:
Data-to-AI lineage
Model validators and examiners ask where an input came from. Lineage turns that from a reconstruction exercise into a lookup.
Contaminated-data blast radius
A bad market-data or customer feed can touch many models. A mapped blast radius shows what to review, retrain or pause.
Unified control crosswalk
Model risk, privacy and AI rules overlap. One control, evidenced once, reduces duplicated testing across teams.
Entry points
Where engagements usually begin
In financial services, a first engagement often centers on model-risk governance and an AI inventory, supported by lineage on the data behind the highest-risk models. Most organizations start from AI governance and bring in data governance as the chain extends. A fixed-scope diagnostic is the usual first step; see engagements for how that is structured.
Regulatory context
Regulatory context, at a high level
Model risk management guidance from banking supervisors (for example SR 11-7 in the US and OSFI Guideline E-23 in Canada) sets expectations for inventory, validation and monitoring. Privacy and data protection law such as GDPR and PIPEDA applies to customer data used in models, and operational resilience rules affect third-party AI. The EU AI Act treats some credit-related uses as high risk.
This is general context only. Requirements vary by jurisdiction and change over time, so confirm current requirements with counsel and regulators. Alignment with a standard is not certification.
Diagnostic
What a diagnostic would look at here
- Model and AI inventory coverage, including vendor and embedded AI
- Lineage from regulatory and customer-facing models back to source systems
- Quality checks on features and reference data feeding model decisions
- How model risk, privacy and AI control testing overlap today
- Evidence an examiner or auditor could receive without a manual rebuild
Each item is examined against the chain of custody: whether the control exists, whether it runs inside the systems rather than on paper, and whether it leaves evidence a reviewer can follow. Nothing is assumed from documents alone, and findings are written for executives and boards as well as for practitioners.
The output is a current-state maturity view, risk exposure and a prioritized roadmap. Request a diagnostic to scope it for your organization.
FAQ
Frequently asked questions
How does this relate to our existing model risk framework?
It builds on it. aerais connects model inventory, validation and monitoring to the data layer, so the existing framework gains lineage and quality evidence rather than being replaced.
Does aerais validate our models?
No. aerais designs and operates the governance system around models: inventory, controls, lineage and evidence. Independent validation stays with your validation function or qualified third parties.
Where would a financial institution typically start?
Usually with a diagnostic that inventories AI and models, maps lineage for the highest-risk uses and compares current controls with the frameworks you answer to.
Contact
Make your data and AI provable.
Start with a diagnostic: current-state maturity, risk exposure and a prioritized roadmap. We aim to reply within two business days.