Industries
Financial services
Corporate and private banking, capital markets, insurance, payments — regulated environments punish the usual AI playbook. Anything that touches a customer or a filing has to be explainable, auditable and reversible, which changes what is worth building at all.
What I do here
- Compliance reporting
- Automating the assembly and review of regulatory reporting, with the audit trail treated as a requirement rather than an afterthought.
- Risk operations
- Risk assessment and monitoring workflows where the model supports a human decision and the handover between them is defined.
- Process optimisation
- Removing manual steps from operations that grew by accretion, starting with the ones that cost the most and break the least.
What changes
- Faster reporting cycles without loosening the controls around them
- Decisions a regulator can follow after the fact
- Manual workload reduced where it is safe to reduce it
- A clear answer to “what happens when the model is wrong”
Work in this industry
19 written up — all case studies →- Insurance
Sequencing the data foundation before anyone bought an AI agent
18 weeksOne affiliate, one funded phase, one go/no-go
- Corporate banking
Winning back the last call after two meetings had failed
One call leftGranted on condition of proving business understanding
- Telecommunications
Every clause gets a human verdict
4 scenariosReal contract types, not one cherry-picked document
- Aviation
Twenty days of discovery that priced a year of build
20 person-daysDiscovery that priced a multi-thousand-hour build
- Finance
Process optimisation in financial services
50%Faster compliance reporting
- Capital markets
An agentic core with a human gate on every state change
8 monthsModelled payback on the first-year business case
Other industries
Working in finance?
Thirty minutes, no deck. Tell me what is stuck and I will tell you whether it is the kind of problem I can move.