E-book

GRC platforms vs. Yields for model risk and AI governance

For model risk & AI governance leaders
ebook - GRC vs. Yields
September 7, 2026
AI governance
Model Risk Management

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Many risk and compliance teams already run a GRC platform for enterprise, operational and vendor risk, and extending it to cover model risk and AI governance looks like the simple choice. One taxonomy, one vendor, one login. This two-page guide compares both paths on the dimensions that matter most, so you can make the call with the full picture.

What you'll get in the guide

01

Where model risk outgrows a generic GRC module, and why teams often end up back in spreadsheets alongside it.

02

A side-by-side comparison across five dimensions: model risk depth, multi-jurisdiction regulatory fit, configuration and maintenance, time to value, and adoption by model risk teams.

03

Out-of-the-box regulatory mapping to the EU AI Act, US SR 26-2, UK PRA SS1/23, Canada's OSFI E-23 and DORA.

04

Why Model Risk Management is a pillar of GRC, not a competitor to it, and how Yields feeds consolidated model risk data back through API.

05

The real drivers of total cost of ownership, beyond licence fees.

See Yields in action

Manage your entire model lifecycle with award-winning Model Risk Management technology, purpose-built for model inventory, validation, monitoring and EU AI Act compliance.

About the

Author(s)

Efrem Bonfiglioli Yields
Efrem Bonfiglioli
Model Risk Management Expert

Efrem Bonfiglioli is a seasoned model and AI risk management professional with a passion for advising model developers and validators on best practices for effective model and AI use case management. He has held various roles related to model risk management across multiple lines of defense in leading global banking institutions, covering a wide range of asset classes and risk types. Efrem is a visiting professor at universities in Italy and the UK where he teaches courses ranging from foundational financial subjects to advanced quantitative modelling. He earned his PhD in Financial Mathematics, where he focused on researching the applications of jump-diffusion models in the context of derivatives pricing.

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