Fintech & Insurance Software Development
Underwriting engines, claims automation, and fraud detection built for audit-grade accuracy.
Three places this expertise shows up
Underwriting & Claims Systems
Claims and underwriting workflows unified across the systems they currently touch, with structured handoffs and status visibility instead of manual re-keying.
Fraud Detection Models
Predictive models trained and validated on your own claims data, flagging risk before payout instead of after the loss is already out the door.
Core Banking Integrations
Role-based access and audit-grade data integrity built for a regulator's questions, not just a user's.
Move fast and prove every number is right
This is the vertical where “move fast” has to coexist with “prove every number is right” — every workflow decision has a compliance and audit trail attached, and a shortcut that would be a minor bug elsewhere is a reportable incident here.
At Tidewell Underwriting, a single claim touched five disconnected systems, with staff manually re-keying the same data into each one — the delay was pure process friction, not underwriting judgment. We unified all five into one workflow with structured handoffs and status visibility. At Harborline Insurance, fraud review happened manually after payout, meaning confirmed losses were already out the door by the time a pattern was caught — we built a predictive model trained and validated on their own claims data (not a generic pretrained model) that cut fraud losses 18% by flagging patterns before payout, not after.
Across these engagements, the architecture leans on PostgreSQL for audit-grade data integrity, Python/PyTorch for models evaluated against known-correct answers before launch, and role-based access built for a regulator's questions, not just a user's.
Work we've shipped like this
Tidewell Underwriting
Automated claims intake and routing across five previously disconnected systems.
5 → 1
Systems requiring manual re-entry
~65%
Reduction in claim intake processing time
0
Weeks of downtime during rollout
Harborline Insurance
A predictive risk-scoring model that flagged high-risk claims before payout, cutting fraud losses 18%.
18%
Reduction in fraud losses
Pre-payout
Flagging now happens before payout, not after
<5%
False-positive rate on flagged claims