A change model for bringing AI into margin and collateral management
How does a traditional financial institution go from a promising AI idea to daily use? Working in a team of three, Peter Cederholm developed a change model that links one concrete use case – forecasting margin calls – to leadership, people, ethics and step-by-step roll-out.
Traditional financial institution (anonymised use case)
2024
Sweden
Concept work: AI adoption and change management
The challenge
Cash and securities used as collateral were managed manually, often based on professional judgement. An AI solution that forecasts margin calls could keep the right levels of cash and securities while meeting regulatory requirements. But the change would affect structure, processes and – above all – people in trading and operations.
Our approach
- A use case defined with why, what, how and who – and the type and size of the change: structural, process and human.
- The current and future flow of trades and margins, showing where AI shortens the cycle.
- A change journey in three phases: prepare, change and reinforce – from awareness and desire to knowledge, ability and lasting use.
- Clear roles: a steering committee, a change team and an AI ethics committee.
- A safe path to production: an AI “playground”, a sandbox and test environment, integration with legacy systems, then roll-out and expansion.
- Education, stakeholder involvement and a cultural analysis, with attention to leadership style.
Results and deliverables
A change model that connects a technical AI idea to people, processes and accountability.
A phased roadmap from first test to production and expansion.
A governance set-up with clear decision rights and ethical oversight.
What do you need to move forward?
20 minutes, free of charge. We understand your situation and goals, give concrete guidance on the spot and assess together whether we can help. If there is a fit, the next step can be a Process Review.