The BackgroundOrganisations are adopting AI faster than they're building the human judgement to check it.
AI adoption in financial services has outpaced the human oversight built to catch it going wrong.
It may sound controversial, however it’s the premise every regulator is currently working from.. and most training hasn’t caught up.
Here’s the gap we exist to close:
Leaders need evidence that output is being checked, not just used
Responsible use of AI and data is not a control in itself.
What matters is how and when the output gets challenged, who is accountable for it and what happens when things go wrong - in practice, not in principle.
Practitioners need methods, not just sanctions
Telling your teams “Don't just trust the AI" doesn’t sound too empowering, does it?
The core of our trainings are methods and frameworks that help to interrogate and question in a structured way.
Teams should rehearse failure before it's real
Spoting a fabricated figure or a badly fitted model is a precious skill worth learning.
Our exercises let teams fail safely, in a workshop and with a facilitator — instead of failing expensively, in front of a client or a board.
The skill gap isn’t technical
Every professional services firm has invested heavily in dashboards, models and automated pipelines — and almost none of them have invested equivalently in teaching people how to challenge what those tools produce.
Your analysts can build a Power BI report - what they can't always do is tell you when the report simply doesn’t make sense. That's the gap this training closes, and it's a gap every regulated or client-facing function has, whether they've admitted it yet or not.
Regulated Industries
In financial services, fintech and other regulated sectors, "good enough" has a more specific meaning: good enough to hand to a regulator.
Across our training, we map our frameworks and use cases directly onto the standards you're already accountable to, including:
PRA SS1/23 and model risk management expectations,
EU AI Act, Annex III high-risk system obligations,
IIA Global Internal Audit Standards and the IIA AI Auditing Framework,
COSO and NIST risk frameworks,
The Three Lines Model and its oversight accountabilities
We also build sessions around specific regulated workflows: credit decisioning, AML transaction monitoring, model deployment, Board reporting — to ensure the exercise isn't about the abstract data and AI risk but the exact judgement call your teams face.
Ask us about sector specific offer for regulated institutions.