What it is
Automates risk data extraction and pre-screening for underwriters.
Business problem
Underwriters spend hours aggregating risk data from PDFs, emails, and third-party sources.
How the solution works
Automate data extraction and risk pre-screening with ML; flag anomalies and generate underwriter-ready summaries inside the PAS.
Typical workflow
1) Ingest & normalize data (APIs/files/forms)
2) Extract/transform key fields
3) Apply rules/models with guardrails & exceptions
4) Write back to systems and notify stakeholders
5) Monitor quality and iterate
Expected outcomes
25–40% faster policy issuance.
Key success metrics (to validate)
- Policy decisions 25–40% faster
- Submission-to-bind time reduced
- Accuracy/consistency improved
Data & integrations
e.g., Policy/Claims Admin (e.g., PAS/claims system), DMS/e-signature, Data Warehouse/BI
Risks & controls
- Data privacy/PII minimization & redaction
- Explainability, audit logs, versioning & rollback
- Model drift monitoring; thresholds for human review
- Experiment governance (holdouts, approval gates)
Implementation path (SprintOps)
- Audit & opportunity mapping (2–4 wks): data readiness + KPI baselines
- Pilot (4–8 wks): narrow scope, measurable KPIs, human-in-the-loop
- Scale (ongoing): expand coverage, harden integrations/MLOps, governance
ROI (example, static)
Inputs to capture: volume, time saved per unit, loaded $/hr, monthly cost
Illustrative formula: Savings = Volume × Time Saved × $/hr; Net ROI = Savings − Cost
FAQ
- Will this replace people? It removes low-value tasks; staff focus on exceptions and customer impact.
- How do you ensure accuracy/governance? Sampling QA, audit logs, change control, and fallbacks.
- How long does a pilot take? Typically 2–8 weeks depending on scope and data readiness.