Inaza Blog

Address Missouri’s tornado, flood, and urban risks. Learn floodplain data enrichment, automated discount enforcement, and ML-driven claims automation to reduce losses.

Meet Colorado’s mountain and winter challenges. Use elevation-based modeling, AI-safety feature scoring, and snow-and-ice FNOL automation to keep MGAs agile.

Tackle Maryland’s Beltway traffic and DOI updates. Apply AI-driven urban-risk scoring, proof-of-purchase automation, and BI-claim severity ranking to refine underwriting and speed claims.

Handle Michigan’s no-fault reform and rate changes with AI. Learn about automated PIP checks, loss-run analysis, and AI-driven injury workflows to turn reforms into competitive advantages.

From Cleveland congestion to lake-effect snow, equip Ohio MGAs with best practices: automated proof-of-insurance checks, crash-test data, and AI BI-claim triage for faster FNOL.

Cover New Jersey’s auto risks from traffic congestion to flood exposure. Learn how VIN decoding, smart verification, and coastal-claim automation improve quoting and accelerate payouts.

Examine Florida’s unique auto risks—hurricanes and surge pricing. Find best practices for automated underwriting, predictive loss modeling, and claims pack automation to ensure timely, accurate payouts.

A practical guide to automating submission intake, including data extraction, triage, and routing, to help carriers improve speed and submission quality.

Quoting speed now determines who wins business in insurance. This post shows how AI helps underwriting leaders cut processing by 90%, scale capacity 5x, and improve loss ratios—without hiring more staff.

Automate FNOL for hurricane-related South Carolina claims with AI. Accelerate first notice of loss handling and improve satisfaction.

Automate FNOL for tornado-damaged vehicles in Oklahoma. Accelerate loss reporting and improve customer satisfaction.

Automate FNOL for heat-damage claims in Arizona. Accelerate first notice of loss and improve customer satisfaction in desert markets.