Data and AI for insurance
Insurance runs on data that is old, deeply nested and heavily governed. Policy administration, claims and actuarial systems were rarely designed to be read together, and the interesting questions almost always span all three.
Clients in this sector: Manulife, Definity
What usually gets in the way
- Policy, claims and billing data living in separate systems of record with no shared key
- Decades of historical data whose schema and business meaning have shifted several times
- Model governance and explainability requirements on anything touching pricing or claims decisions
- Reinsurance and regulatory reporting that must reconcile exactly, not approximately
- Personally identifiable and health information constraining where data can be processed
What we build
- Claims and policy data pipelines with lineage that survives an audit
- Actuarial and pricing feature stores that analysts can query without re-deriving joins
- Fraud and anomaly detection with reason codes attached to every flag
- Reserving and reporting automation that reconciles against the ledger
- Model monitoring: drift, stability and challenger comparison in production
How we handle the constraints
Anything influencing pricing, underwriting or a claims decision needs to be explainable to a regulator and reproducible after the fact. We build the audit trail as part of the system rather than bolting reporting on at the end.
Ways to work with us
Fixed-scope build
Defined deliverable · weeks to months
The problem is well understood and you want it solved without opening a hiring req.
Embedded senior team
Ongoing · alongside your people
You have a capable team that is short on senior capacity, not on direction.
Advisory and roadmap
Assessment · sequenced plan
There are several plausible directions and the cost of picking the wrong one is high.
Build and run
Delivery · then ongoing operation
The system matters but does not justify hiring a permanent team to maintain it.
Common questions
Can you work inside our environment rather than moving data out?
Yes, and it is usually the default for insurance work. We build in your cloud tenancy, under your access controls and your review process. Where data cannot move, the work comes to the data.
How do you handle model explainability for regulated decisions?
Reason codes, feature attribution and a reproducible training record are part of the deliverable, not an afterthought. If a decision cannot be explained to a regulator, it is not finished.
Working on something in insurance?
Describe the problem in a couple of sentences. If we're not the right people for it, we'll tell you who is.