R

Regional P&C Carrier (anonymous)

Regional P&C Carrier Achieves 369% ROI in 12 Months with Claims and Underwriting Automation

368.9%12-Month ROI
$3,182,670Annualized Run-Rate Savings
15.4 weeksPayback Period

The Challenge

A regional P&C carrier with ~$650M GWP and 1.2M active policies operated on legacy PAS/claims systems with document-heavy, manual workflows and siloed data. Limited reporting capabilities, rising cyber and privacy requirements, and inconsistent data quality (duplicate parties, mismatched IDs, PDFs/emails) were driving up the expense ratio and slowing claims and underwriting throughput.

The Solution

JetBridge embedded a tiger team to build a governed data backbone with incremental ingestion from PAS/claims, billing, CRM, and call transcripts, plus entity resolution and analytics marts. Workflow automation covered document classification and field extraction from PDFs/emails, severity-based triage routing with adjuster-ready summaries, and SIU anomaly scoring. Governance included RBAC, immutable audit logs, drift monitoring, and rollback playbooks — delivered over a 10-week pilot followed by a 6-month rollout.

Results

The engagement generated $3.18M in annualized run-rate savings and $1.03M in annualized revenue lift, for a 12-month net benefit of $3.31M against a $897K total team cost. The payback period was 15.4 weeks and the 12-month ROI was 368.9%. Savings were anchored to reductions in manual touches, cycle time, leakage capture, and lowered rework, with governance reducing audit prep time.

Key Takeaways

  • A defined, fixed-scope pilot (6–10 weeks) with a locked scorecard is an effective way to validate ROI assumptions before committing to a full rollout.
  • Governing the data backbone (entity resolution, immutable audit logs, RBAC) is as important as the automation layer — it enables auditability and repeatable controls across releases.
  • Mid-market carriers can achieve enterprise-level automation outcomes without permanent headcount by embedding a senior tiger team that hands off documentation, dashboards, and ownership at rollout end.

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Details

Use Case
Document & Data Processing
AI Technology
NLP
Company Size
MidMarket
Quality
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