Zinnia collaborates with Snowflake on real-time insurance analytics and AI
Zinnia has integrated its insurance platform with Snowflake's AI Data Cloud and rolled out Snowflake's data platform across its operations. The goal: deliver real-time metrics, embedded ML and AI, and a cloud data foundation that actually moves the needle for insurers.
Zinnia will also serve as an implementation partner for carriers adopting Snowflake-pairing industry expertise with a platform built for scale and security. That means faster stand-ups, fewer integration headaches, and a clearer path to production.
What this means for insurers
- Real-time business metrics and self-serve analytics across distribution, policy admin, claims, and service.
- Enterprise-grade security and scalability for sensitive policyholder data.
- Predictive analytics, risk modeling, and automated decisioning embedded in workflows.
- Ability to build self-hosted analytics apps with Streamlit and centralize data in the cloud.
- Secure data sharing between carriers and partners to speed up insight generation and decision-making.
Dan Gremmell, Chief Data Officer at Zinnia: "Insurance companies are sitting on vast amounts of data but many struggle to unlock its full potential. Our work with Snowflake changes that equation entirely.
"We are delivering day-one implementation of advanced analytics and AI capabilities that would typically take months or years to deploy, ensuring our clients don't just receive data - they transform it into their competitive advantage."
Sean O'Donoghue, CTO at Security Benefit: "Snowflake's data sharing paradigm allows Security Benefit and Zinnia to securely exchange vast amounts of information on demand. This empowers us to analyse data, generate insights and make faster, more informed decisions."
Practical use cases you can deploy now
- Underwriting triage: route cases based on risk signals, missing requirements, and cycle-time risk.
- Lapse and surrender prediction: target at-risk segments with timely retention actions.
- Claims: fraud propensity scoring and straight-through processing where confidence is high.
- Distribution: next-best action for agents, lead scoring, and appointment optimization.
- Finance/ALM: scenario testing, expense ratio tracking, and premium adequacy monitoring.
- Customer service: generative AI assistance for inquiries, policy changes, and documentation.
How the stack fits together
- Snowflake AI Data Cloud for centralized, governed data and ML/AI workloads (learn more).
- Streamlit for building internal, self-hosted analytics apps and decision tools on top of shared data (official site).
- Secure data sharing between carriers, reinsurers, and partners to cut file transfers and stale extracts.
Implementation notes
- Start with a clear data model for policy, claims, and distribution events. Keep lineage and definitions simple and documented.
- Tokenize PII and set strict role-based access. Audit everything. Automate retention policies.
- Prioritize 2-3 high-ROI use cases. Ship an MVP in 60-90 days, then iterate with real feedback.
- Keep a human-in-the-loop for underwriting and claims decisions until model stability is proven.
- Set measurable targets: cycle time, placement rate, loss ratio impact, call handle time, and expense savings.
Why this partnership matters
Data in life and annuity businesses is fragmented across legacy systems and partner networks. Zinnia's role as an implementation partner helps carriers bridge that gap and accelerate time-to-value on Snowflake without months of plumbing.
For teams under pressure to improve growth and profitability, this is a direct path to production-grade analytics and AI-minus the usual delays.
Next steps for your team
- Pick one line of business and define the top five "always-on" metrics you need in real time.
- Stand up a secure data share with your key partner or reinsurer to reduce file exchanges.
- Build a Streamlit app for a single decision point (e.g., underwriting triage) and expand from there.
- Operationalize model monitoring from day one: data drift, bias checks, exceptions, and overrides.
If your team needs upskilling to operate Snowflake, build Streamlit apps, or apply generative AI safely in insurance workflows, explore practical programs by job role here: Complete AI Training.
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