THE CHALLENGE
Model Inventories Are
Outgrowing Model Risk Teams
Every new pricing, fraud or GenAI model adds to an inventory that MRM teams struggle to validate and monitor. ML and GenAI also introduce new risks around bias, hallucination and drift.
Anaptyss extends your MRM capacity with trained specialists who work within your existing methodology, keeping validation, tiering and monitoring on track.
Model Risk Capacity That
Scales With Your Inventory
We deliver validation, tiering, monitoring, and reporting integrated with your methodology, templates, systems, and thresholds. Automation handles repeatable evidence and documentation work, while model risk officers retain ownership of opinions and sign-off.
WHAT WE DO
Model Risk Work We
Run End to End
Independent
Model ValidationFull-scope review of a model's theory, data, methodology and output, closed out with a validation report and opinion your model risk committee can act on.
Model
Development reviewA conceptual soundness check before a model reaches production: is the problem definition right, is the design defensible, does the methodology hold up.
Inventory and
TieringBuilding and maintaining the model inventory, drawing clean boundaries between models and end-user tools, and tiering by risk so effort goes where it matters most.
Ongoing Monitoring
and TestingBacktesting, sensitivity analysis and stability checks run on schedule, with breaches routed against your escalation thresholds, not discovered at the next annual review.
GenAI and ML
OversightBias, hallucination and explainability testing built for models that do not behave like traditional statistical ones, with drift monitoring that catches degradation early.
Post-Model
Adjustment ReviewRevisiting overlays and manual adjustments on a set cadence, so what started as a temporary fix doesn't quietly become permanent without documented justification.
Regulatory and
Policy SupportKeeping your validation standard and MRM policy current, and preparing the file before an examiner asks for it, under SR 11-7, TRIM, SS1/23 or your local equivalent.
Committee and
Board ReportingTurning validation status and open findings into reporting your model risk committee and board can actually use to steer.
Delivered Through Digital Knowledge Operations™
We adopt your MRM methodology, systems and thresholds before validation begins, then extend your team with additional capacity while keeping ownership of model decisions with your organization.
- 01
Integrated MRM
MethodologyMRM policies, tiering logic, and templates are integrated into our specialist training, assessment, and deployment, evolving as requirements change.
- 02
Continuity Over
RotationThe same validators and reviewer stay on your inventory over time. They get to know your models and business lines well enough that reviews get faster, not just repeated.
- 03
Automation Handles the
Repeatable WorkANA automates structured testing, evidence review and documentation against your validation standard, with an audit trail and human sign-off.
- 04
Retaining Model
Risk OwnershipTesting and findings are documented for your review. Model risk officers retain final opinion and sign-off, with full analysis and evidence records preserved for every file.
Enhancing Model Risk Functions
Remediation is tracked through completion, so open findings don't simply roll forward on the inventory.
Evidence gathering and documentation transition to the managed team, allowing validators to focus exclusively on methodology, analysis, and findings.
As ML and GenAI models grow, validation capacity scales with the inventory instead of trailing behind it.
Documentation built during validation ensures that examinations and reviews rely on an established record, streamlining the audit process.
- 40%
Faster validation of third-party credit risk scorecard models for a US commercial lender, using a machine learning-powered validation solution.
- 50%
Faster response time for a US payments fintech, using the same AI automation Anaptyss applies to model risk documentation.
- 100%
Quantification of model performance across accuracy, precision, recall and fairness metrics for a US bank's FINCRIME model suite, ensuring MRM policy compliance.
- USD400,000
Annual savings for a US commercial lender through a machine learning-based credit risk scoring model.
Related Insights
Long formWhitepapers
Layered research on governance, model risk, and controls.
WeeklyBlog
Short signals from teams building AI inside regulated walls.
Practitioner guideseBooks
Step-by-step playbooks you can hand to an operating team.
Live & on demandWebinars
Sessions with practitioners, broadcast and archived.
See Where Your Model
Inventory Actually Stands
Send us your current inventory and validation backlog. We'll tell you which models are
overdue, which are misclassified, and what a managed team would clear first.