Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
MIT AI Risk Repository For Fintech Governance
Apr 29, 2026 | NCFA Resource | Artificial Intelligence And Data, Risk Compliance And Regtech
AI Risk Taxonomy For Audits And Controls
The MIT AI Risk Repository is an open database created by researchers at MIT to bring structure to AI risk. It compiles more than 1,700 documented risks from 74 existing frameworks and studies into a single system. The aim is practical. AI risk guidance exists, but it is scattered and inconsistent across sources. This repository organizes it into a shared taxonomy, with links that show how risks connect and compound across systems.
See: Age Checks Become Digital Compliance Infrastructure
In practice, this gives teams a consistent way to map risk across AI systems.
- A lending team can trace where bias or model drift may affect credit decisions
- A fraud team can test how detection gaps might cascade into losses
- Compliance teams can align internal reviews to a shared structure instead of stitching together multiple frameworks
- It brings product, engineering, and risk teams onto the same page as firms scale AI into production and face tighter audit expectations, similar to how financial institutions approach model risk management in production environments
Who Gets Value
Teams already running AI in production will get the most from this. If you’re operating models in lending, fraud, onboarding, or customer support, it gives you a structured way to think about risk across systems. Larger fintechs and financial institutions dealing with audit and regulatory pressure will find it useful quickly. Early stage teams without deployed models will likely find it heavy and not immediately relevant.
Strengths And Limits
The strength here is structure. It turns fragmented AI risk concepts into something teams can actually use, and the causal links add depth that most frameworks miss. At the same time, it does not rank risks by likelihood or impact, and it does not translate directly into controls or regulatory compliance. Some classifications reflect interpretation across sources, and emerging risks may not be fully captured. Teams still need to apply judgment and build their own control layer on top.
Key Resources
Repository Homepage (AI risk overview and navigation)
Full Risk Database (AI risk dataset for audits)
Causal Taxonomy (AI risk relationships mapping)
Domain Taxonomy (AI risk classification framework)
Research Paper (AI risk methodology and design)
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