James River adopts Kalepa's AI underwriting platform to sharpen E&S execution
10 February 2026
James River Insurance Company has chosen Kalepa's professional-grade AI platform to improve underwriting efficiency across its excess and surplus (E&S) lines. The insurer, a subsidiary of Nasdaq-listed James River Group Holdings, plans to use the platform to speed up quoting, strengthen risk selection, and tighten portfolio management. The move fits the carrier's push to use technology that supports long-term, profitable growth and a clearer edge in the E&S market.
What the platform does
Kalepa's platform supports the full underwriting workflow. It automates submission intake, triages risks based on appetite and exposure, and consolidates risk-critical data from multiple sources to produce decision-ready quotes. By pairing automation with risk intelligence, underwriters get more context with fewer manual steps and shorter cycle times.
Why this matters for E&S underwriters
E&S submissions are messy, inconsistent, and time-sensitive. Speed without discipline drives leakage; discipline without speed loses the account. A system that standardizes intake, flags key exposures, and keeps decision logic consistent gives underwriters time back for judgment calls where it counts. Expect cleaner pipelines, tighter appetite control, and better hit-rate economics.
Leadership view
James River's leadership frames the investment as a practical lift to disciplined underwriting and employee tooling. The expectation: faster, more consistent decisions with deeper insight-improving risk selection and overall portfolio performance. The technology choice also reflects a broader push for AI-enabled operations and continuous improvement, with Kalepa selected for its integrated approach, underwriting know-how, and track record of measurable results.
Company context
James River Group Holdings operates in two specialty P&C segments: E&S lines and specialty admitted insurance. Its regulated insurance subsidiaries hold an A- (Excellent) Financial Strength Rating from AM Best rating guide. Kalepa, founded in 2018, is backed by IA Ventures and Inspired Capital.
What to watch next
- Integration scope: Which LOBs, classes, and broker channels go first; how the platform fits policy admin, rating, and data lakes.
- Workflow impact: Submission routing, appetite screening, clearance, and broker communications-especially for incomplete or high-friction risks.
- Data foundation: Source systems, third-party data rights, data quality checks, and a clear system of record for underwriting artifacts.
- Triage and rules: Who sets appetite rules, how exceptions are handled, and how overrides are captured for audit.
- Model performance: Monitoring win rate, quote-to-bind, cycle time, and loss ratio by segment; feedback loops for continuous tuning.
- Governance: Human-in-the-loop checkpoints, documentation, and controls that satisfy compliance, audit, and broker transparency.
- Security and privacy: Treatment of sensitive submissions, PII, and retention policies across integrated systems.
Practical steps for carriers and MGAs
- Run a focused pilot: Pick one class with enough volume and loss history to measure impact within 60-90 days.
- Clean the intake: Standardize broker templates, enforce required fields, and auto-enrich with authoritative data at the door.
- Define decision rights: Map where AI suggests, where it auto-advances, and where underwriters must review.
- Instrument the pipeline: Track quote turnaround, submission-to-quote, quote-to-bind, and bind-to-issue by broker and class.
- Train the team: Short sessions on new workflows, exception handling, and how to document rationale for complex risks.
- Scale in waves: Expand by class and broker cohort once KPIs clear thresholds for speed, hit rate, and loss ratio.
For teams building AI fluency across underwriting and operations, you can explore role-based learning paths here: Complete AI Training - Courses by Job.
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