Ai Solutions
Ai Solutions
A mid-market insurer processed 12,000 applications monthly with a 14-day average turnaround. Manual underwriting created bottlenecks and inconsistent risk assessment.
A mid-market insurer was drowning in paper. 12,000 applications per month, 14-day turnaround, and underwriters spending 60% of their time on data entry rather than risk judgment. The operations team knew automation was the answer; every prior attempt had failed because it tried to replace the underwriter instead of removing the busywork.
We started with a two-week discovery sprint embedded with the underwriting team. The core insight: 64% of applications were straightforward enough for fully automated decisions, but the existing system could not identify which ones. We rebuilt the workflow so the model triaged first, then routed only the genuinely complex cases to humans with structured recommendations attached.
Three-layer system: (1) NLP document extraction pipeline using a fine-tuned BERT variant for insurance-specific entities; (2) gradient-boosted risk scoring model trained on 5 years of claims data with calibrated probability outputs; (3) LLM-powered recommendation engine that generates structured summaries for the 36% of cases requiring human review. Every model call is logged with feature inputs and outcomes for audit.
18 weeks from kickoff to production. Weeks 1-4: data audit and model baseline. Weeks 5-10: pipeline development. Weeks 11-14: integration and UAT. Weeks 15-18: phased rollout.
Six months post-launch: straight-through processing rate at 64%, average turnaround 4 hours, zero compliance incidents, underwriter satisfaction scores up 40%. The underwriter team was redeployed to specialty lines where their judgment actually moved the needle.
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