Earnix unveils AI-driven decisioning suite for insurers
At its Excelerate 2025 conference, Earnix introduced a set of AI-based solutions built to speed decision-making, personalize customer interactions, and meet the demands of regulated markets. The centerpiece is the Intelligent Decisioning platform, aimed at helping carriers act on data at scale with real-time insight and controlled automation.
What the Intelligent Decisioning platform brings
- Real-time analytics across high-volume data to support pricing, underwriting, and customer decisions.
- Governance features to oversee automated decisions, with auditability and explainability.
- No-code and low-code tools so business teams can adjust strategies without heavy IT lift.
- Integration with existing data systems and engagement channels to keep workflows intact.
- Compliance controls to operate within strict regulatory frameworks.
New tools announced
- AI Studio: A workspace to create and manage AI agents inside decisioning workflows, with controls for versioning, authorization, and performance monitoring.
- Elevate Data: A managed data layer to integrate and transform proprietary and third-party data, simplifying model development and decision support.
- Customer Engagement Platform updates: AI-driven recommendations that guide sales and service teams in real time to improve conversions and service quality.
Why this matters for carriers
Risk moves fast, and pricing precision is table stakes. Earnix's approach combines predictive, generative, and agentic AI inside structured workflows, giving teams speed with oversight.
- Underwriting and pricing: Faster model deployment, more frequent rate adjustments, and tighter alignment to risk signals.
- Distribution and service: Next-best-action guidance for agents and contact centers to lift conversion and retention.
- Governance and audit: Clear lineage for data, models, and automated decisions to satisfy internal policies and regulators.
- Speed to market: Business-led changes via no-code/low-code reduce dependency on release cycles.
Industry perspective
Earnix leadership emphasized a focus on AI with guardrails: bringing predictive, generative, and agentic methods together while maintaining governance and explainability. Product leadership highlighted the need for controls that let carriers run agentic AI inside decision flows with reliability.
Analyst commentary from Celent points to a broader trend: embedding advanced AI and automation into core processes to improve risk assessment, accelerate innovation, and deliver more responsive customer experiences. For regulatory guidance on trustworthy AI, see the NIST AI Risk Management Framework here.
Practical steps to get value fast
- Data readiness: Map priority data sources (policy, claims, telematics, third party). Define a minimum set for each target decision and address quality issues early.
- Model governance: Establish approval paths, versioning, monitoring thresholds, and documentation standards. Align with your model risk policy and internal audit needs.
- Pilot smart: Start with one use case (e.g., renewal pricing or next-best-offer) and measure impact: time-to-rate change, loss ratio shift, quote-to-bind, and service metrics.
- Change management: Give underwriters, pricing teams, and front-line staff clear playbooks and feedback loops. Adjust recommendations based on observed outcomes.
- Integration plan: Connect decisioning to policy admin, rating, CRM, and engagement channels so insights translate into action without rework.
Bottom line
Earnix is packaging predictive, generative, and agentic AI with controls that matter to insurers-governance, explainability, and compliance-while keeping business teams in the driver's seat. For carriers, the path forward is clear: pick high-impact decisions, connect the data, enforce guardrails, and iterate quickly.
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