
What Insurance Teams Build with Vellum
Use-cases

What Insurance Teams Build with Vellum
Use-cases

What Insurance Teams Build with Vellum
What Insurance Teams Build with Vellum

Transformation example
Insurance Claims AI Transformation Journey

Defines claims processing logic, fraud detection criteria, and business rules through Vellum's visual interface. Can approve pilots up to $75K; requires CFO approval for large initiatives. Must demonstrate ROI in 24 months and maintain regulatory compliance.

Builds production infrastructure, integrations, and ensures enterprise-grade reliability and compliance. Can architect solutions but requires InfoSec, Compliance, and MRM sign-off. Must maintain 99.9% uptime and complete audit trail compliance.
What Insurance Teams Build with Vellum
Transformation example
AI Medical Chatbot: Patient Intake

Designs intake logic and ensures clinical safety. Defines escalation paths (emergency vs non-emergency) and validates AI prototypes against compliance standards. Requires ongoing consultation with hospital ethics committee and medical staff leadership.

Builds integrations into EMR systems and ensures secure data handling. Implements production-grade infrastructure for patient data. Must maintain audit logging, uptime, and strict regulatory compliance (HIPAA, SOC-2).
What Insurance Teams Build with Vellum
Internal governance before operational deployment
- Lisa Chen, Claims Director: Defines a conservative pilot for personal auto claims in Texas, targeting 8–12% efficiency gains and securing $65K approval with clear success criteria.
- David Rodriguez, AI Engineering Lead: Builds the compliance-ready foundation using Vellum’s audit trails, monitoring, governance, and data lineage to cut prep time in half and ensure traceability.
- Maria Santos (Model Risk Management): Uses Vellum’s evaluation, bias testing, and execution tracking to validate models, set performance baselines, and document MRM compliance.
- James Wong (Chief Risk Officer): Approves a conservative pilot scope with human-in-the-loop validation, weekly performance monitoring, and clear exit thresholds to manage risk.
- Patricia Kim, Information Security Lead: Oversees claims system integration with Duck Creek, implements encryption and access controls, and leverages Vellum’s built-in security architecture to cut custom security work and meet a 6-month integration timeline.
- Robert Chen, Compliance Officer: Focuses on audit readiness by using Vellum’s audit trails, traceability, and documentation standards to prepare for regulatory examinations and ensure transparent customer communication.
Conservatively scoped, compliance-ready pilot with MRM validation, CRO risk controls, secure system integration, and full audit documentation.
Risk and use-case validation with measured learning
- Controlled rollout: Gradual AI adoption with human override, moving from full manual decisions to limited autonomy on low-value claims.
- Performance gains: By month 24, processing time drops 15%, fraud detection improves 6%, and cost per claim falls 10%.
- Change management impact: Adjuster satisfaction rises from 48% to 65% with structured adoption and oversight.
- Compliance assurance: Zero AI-related complaints, full audit trails, and bias testing show fairness and transparency.
- Quality maintained: Error rate steady at 2.3% and customer satisfaction sustained at 71%.
6,200 claims processed with $89K annual savings, reflecting a 14% cost improvement. Error rate steady at 2.3% and customer satisfaction sustained at 71%.
Careful scaling with state-by-state adaptation
- Expanded to property claims under $15K with 70% workflow reusability and adaptation to regional fraud patterns, reaching 14,000 AI-assisted claims annually by Month 42.
- Processing time cut to 8.2 days, fraud detection improved to 76%, and annual savings increased to $285K.
- 6–9 months of adaptation for state regulations, fraud patterns, and local adjuster training, with Vellum’s governance framework supporting compliance and customization.
- Different legacy system setup, API limitations, and regional performance issues require targeted engineering and optimization.
Multi-state AI claims system delivering faster processing, higher fraud detection, and $285K annual savings in Texas, while adapting workflows and integrations for Florida expansion.
Competitive differentiation through accumulated intelligence
- Predictive severity modeling, subrogation detection, seasonal damage protocols, and vendor analytics become part of the workflow after 4+ years of Vellum use.
- Publish benchmark reports and whitepapers based on 95,000+ processed claims
- Collaborate with insurers, healthcare providers, and technology vendors on AI standards
Market-leading claims AI program that delivers faster processing, higher fraud accuracy, lower costs, and industry recognition.
Internal governance before operational deployment
- Dr. Sarah Patel, Clinical Director: Defines a conservative pilot for intake queries in California, targeting 8–12% intake efficiency gains and securing $60K approval with clear success criteria.
- Mark Alvarez, AI Engineering Lead: Builds the compliance-ready foundation using Vellum’s audit trails, monitoring, governance, and data lineage to cut prep time in half and ensure HIPAA traceability.
- Maria Lopez, Model Risk Manager: Uses Vellum’s evaluation, bias testing, and execution tracking to validate intake models, set performance baselines, and document MRM compliance.
- James Wong, Chief Risk Officer: Approves a conservative pilot scope with human-in-the-loop validation, weekly performance monitoring, and clear exit thresholds to manage patient safety risk.
- Patricia Kim, Information Security Lead: Oversees integration with the provider directory and EHR, implements encryption and access controls, and leverages Vellum’s security architecture to cut custom security work and meet a 6-month integration timeline.
- Robert Chen, Compliance Officer: Focuses on HIPAA readiness by using Vellum’s audit trails, traceability, and documentation standards to prepare for regulatory examinations and ensure transparent patient communication.
Compliance-ready pilot with MRM validation, CRO risk controls, HIPAA-secure integration, and full audit documentation. Implementation included 6-month physician training program, ongoing clinical advisory board oversight, and quarterly compliance audits with healthcare legal counsel.
Risk and use-case validation with measured learning
- Controlled rollout: Gradual chatbot adoption with clinician override, moving from manual triage to limited autonomy for low-risk intake queries.
- Performance gains: By month 24, intake time drops 15%, misclassification rate improves 7%, and admin cost per intake falls 12%.
- Change management impact: Clinician satisfaction rises from 52% to 68% with structured adoption and oversight.
- Chatbot-related complaints reduced to less than 0.5% of interactions, with all complaints resolved within 24 hours through established escalation protocols.
- Intake accuracy maintained at 96% for routine cases, with complex cases requiring clinical review maintaining separate accuracy metrics.
8,500 patient intakes processed with human oversight for 30% of complex cases, achieving $95K annual savings primarily through administrative efficiency gains rather than clinical workflow changes.
Careful scaling with state-by-state adaptation
- Expanded to mental health and primary care intake with 70% of core technical infrastructure reusable across states, with significant adaptation required for local provider networks, insurance relationships, and regulatory variations.
- Intake processing time cut to 6.8 minutes, classification accuracy improved to 97%, and annual savings increased to $310K.
- 6–9 months of adaptation for local regulations, insurance networks, and provider availability, with governance supporting compliance and customization.
- Different EHR setups and API limitations require targeted engineering and optimization.
Multi-state AI chatbot delivering faster intake, higher accuracy, and $310K annual savings in California, while adapting workflows and integrations for Florida rollout.
Competitive differentiation through accumulated intelligence
- Predictive care triage, insurance eligibility verification, appointment optimization, and population health analytics become part of the chatbot workflow after 4+ years of Vellum use.
- Collaborate with healthcare research institutions to publish peer-reviewed studies on AI-assisted intake efficiency, following IRB approval and patient consent protocols for any research publications.
- Collaborate with hospitals, insurers, and regulators on AI safety standards.
Market-leading AI medical chatbot program delivering faster patient intake, higher accuracy, lower costs, and recognized as an industry benchmark.
Secure AI Development with Cross-Team Collaboration
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Proof from Insurance Teams


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