Gradient AI Partners with Connexure to Streamline Risk Management Workflows for Carriers and MGUs
For executives in insurance, speed and precision decide margin. This partnership signals a cleaner handoff between data, underwriting judgment, and distribution-so teams can quote faster, score risk with more context, and cut friction across the submission-to-bind path.
Why it matters for management
- Shorter cycle times: Reduce submission triage and time-to-quote with automated intake and prioritization.
- Better risk selection: Apply consistent, explainable scoring to focus underwriter attention where it counts.
- Operational efficiency: Increase throughput without adding headcount; redeploy talent to complex accounts.
- Governance and auditability: Standardize workflows and preserve a clear trail for reviews and regulators.
What changes in the workflow
- Submission intake: Structured capture of broker submissions and attachments, with data validation upfront.
- Data enrichment: Pull third-party data and internal history to fill gaps and add signal.
- Risk scoring and triage: Rank accounts by likelihood to bind, expected loss, and underwriting effort.
- Underwriter workbench: Route to the right team with context, notes, and required checks in one place.
- Feedback loop: Writebacks of outcomes improve models and refine appetite over time.
Metrics to watch
- Average time from submission to quote
- Quote-to-bind ratio by segment and broker
- Underwriter capacity (quotes per FTE) and touch time
- Loss ratio trend on scored vs. unscored business
- Compliance exceptions and audit findings
Implementation playbook
- Data readiness: Map sources, establish quality thresholds, and define retention policies.
- Model oversight: Set standards for explainability, validation, and drift monitoring. Consider the NIST AI Risk Management Framework for guardrails.
- Integrations: Use APIs for policy, rating, and CRM systems. Enable SSO and role-based access.
- Change management: Train underwriters on scoring interpretation and escalation paths.
Controls that keep you safe
- Human-in-the-loop: Maintain final decision authority with clear override rules.
- Bias testing: Routine fairness checks across segments and geographies.
- Audit trail: Log data sources, model versions, and underwriting decisions.
- Fallback plans: Define manual procedures for outages or anomalous results.
90-day rollout roadmap
- Weeks 1-2: Define success metrics, governance, and data contracts.
- Weeks 3-4: Map current workflows; select 1-2 lines of business for pilot.
- Weeks 5-8: Integrate intake, enrichment, and scoring; run shadow mode; compare outcomes.
- Weeks 9-12: Move to controlled production; train users; monitor KPIs; iterate.
Team responsibilities
- Underwriting: Define appetite, review scores, provide feedback on edge cases.
- Actuarial/Analytics: Validate models, track lift, and set thresholds.
- Compliance: Approve controls, disclosures, and documentation.
- IT/Security: Oversee integrations, access, and data protection.
If your team is building AI fluency to support initiatives like this, explore practical training for managers and operators. Start with AI courses by job role or the AI Automation Certification to accelerate adoption.
For standards that support data quality and interoperability across carriers and MGUs, review ACORD resources as you plan integrations.
The takeaway: use this partnership to remove manual bottlenecks, enforce consistent decisions, and create a measurable lift in growth and profitability-without adding complexity for your teams.
