A governance-first AI rollout for financial services
The firm moved from informal AI usage to a controlled rollout model with clear ownership.
18AI use cases mapped and risk-scored
100%approved live workflows designed with audit trails
Fintech
AI for fintech, with the controls your regulator expects
Blash AI is the artificial-intelligence division of Blash Advisory, a London-headquartered corporate finance and advisory firm building AI systems for fintech businesses across the UK, EMEA and the Far East.
Fintech is where our governance focus matters most. We automate the operations and wrap them in the controls a regulated firm needs.
Book an AI audit → Where the time goes
- Onboarding and document checks done manually
- Support and dispute triage handled by hand
- Regulatory and board reporting assembled each cycle
What we build
- Document extraction and checks for onboarding
- Support and dispute triage with drafted responses
- Knowledge and policy answers grounded in your sources
- Recurring regulatory and board reporting
- Controls, monitoring and audit trails on every workflow
Proof Financial services Fintech Regulated advice See all proof →
What this looks like in fintech
Controlled AI operations for a fintech team
Routine work moved faster while consequential decisions stayed with humans.
65%faster routine response drafting
100%consequential actions gated by human approval
Compliant enquiry intake for a regulated advice firm
More enquiries were captured without crossing advice boundaries.
41after-hours enquiries captured in the first month
96%enquiries logged with consent status and source
Questions
Can AI be used safely in a regulated fintech?
Yes, with the right controls. We build human approval steps, monitoring, and audit trails so the system can be defended to a board or a regulator, and design with FCA operational-resilience and Consumer Duty expectations in view.
Do you handle sensitive data appropriately?
Yes. The system runs inside your own accounts, permissions mirror the access your team already has, and model providers are used under API terms that exclude training on your data. A DPIA is completed before any higher-risk processing goes live, and every action is logged and attributable.
What about model errors?
AI steps fail into an exception queue rather than acting, and autonomy increases only as the measured error rate earns it.
Other industries
Retail →
Retail runs on high volumes of small, repetitive tasks. We automate the ones that drain hours and let enquiries go cold.
Construction →
Construction generates documents faster than people can process them. We automate the reading, extraction and chasing.
Manufacturing →
Manufacturing back-offices run on quotes, orders and quality documents. We automate the handling so people manage the exceptions.
Leisure and Hospitality →
Hospitality lives on enquiries and reconciliations. We automate the response and the reconciliation so nothing goes cold or unmatched.