Core Relationships: For the Lead - Insurance Underwriting & Loss Ontology, the core relationships and stakeholders reflect the groups that create, manage, consume, analyze, and govern underwriting and loss-related data across the enterprise.
1. Underwriting Leaders & Teams: Partner with underwriting leaders and subject matter experts to define exposure, coverage, risk assessment, pricing, and profitability concepts within the ontology. Ensure underwriting requirements, risk evaluation methodologies, carrier submission processes, and portfolio management metrics are accurately represented in enterprise data models and business definitions.
2. Claims Leaders & Teams: Collaborate with claims leadership and operations teams to establish consistent definitions for claims, reserves, payments, recoveries, cause of loss, severity, and loss development metrics. Ensure claims and loss data are accurately represented within the ontology to support claims operations, loss analysis, and performance reporting.
3. Agency, Wholesale & Specialty Business Leaders: Work closely with retail, wholesale, MGA, and specialty business leaders to understand unique underwriting, coverage, exposure, and claims processes across business units. Ensure the ontology accommodates business-specific requirements while maintaining enterprise consistency and standardization.
4. Carrier Partners & Market Relationships: Partner with carrier stakeholders to understand carrier-specific coverage structures, underwriting requirements, loss reporting standards, appetite data, submission requirements, and policy administration practices. Ensure carrier data can be standardized and integrated into the enterprise ontology while preserving critical business context.
5. Data & Analytics Teams: Collaborate with analytics, business intelligence, data science, and actuarial teams to define trusted metrics, dimensions, derived attributes, and business logic. Ensure underwriting, exposure, coverage, and loss data can be consistently leveraged for reporting, forecasting, profitability analysis, AI, and advanced analytics initiatives.
6. Technology & Enterprise Architecture Teams: Partner with enterprise architecture, application owners, integration teams, and engineering organizations to align source system data with ontology standards. Provide business definitions, mappings, lineage requirements, and domain expertise that support enterprise data products, workflows, and technology solutions.
7. Data Governance & Master Data Management: Work with data governance, MDM, data quality, and stewardship teams to establish authoritative sources, data ownership, governance policies, and quality standards for coverage, exposure, underwriting, claims, and loss information across the enterprise.
8. External Data & Industry Partners: Collaborate with providers of carrier, IVANS, catastrophe, geospatial, valuation, benchmarking, and other third-party insurance data to ensure external data sources are properly understood, standardized, and incorporated into the ontology in support of underwriting, risk management, and loss analysis.
9. Legal, Compliance & Regulatory Teams: Partner with legal, compliance, and regulatory stakeholders to ensure ontology structures and business definitions support insurance regulatory requirements, carrier reporting obligations, audits, certificates, evidence of coverage, and other compliance-related processes.
10. Product, Workflow & AI Solution Owners: Work with product managers, workflow owners, and AI solution teams to ensure coverage, exposure, underwriting, and loss concepts are accurately represented and consumable across enterprise applications, operational workflows, digital experiences, reporting platforms, and AI-powered business solutions.
Core Applications Supported: Provide domain expertise, data alignment, and ongoing support for key applications including the external portals and other core submission and underwriting applications to ensure consistent data usage and adherence to ontology and industry standards. Qualifications Deep expertise in how insurance risk is evaluated, bound, serviced, and ultimately produces loss outcomes. Ideal candidates have operational business knowledge, data governance, analytics enablement, and cross-system data integration experience.