Risk Management
Using Google Earth Engine and AI-driven geospatial modeling to accurately predict wildfire risk enables Delos to offer affordable insurance to California homeowners deemed uninsurable by other carriers.
Reported impact
"Investing in Google's unified technology ecosystem has greatly accelerated product development, improved connectivity, and boosted efficiency across our entire organization," reports Stein.
"The Google Earth Engine data catalog offers an easily accessible repository of uniformly formatted landscape and weather data, eliminating the burden of manual data cleansing and ensuring data consistency throughout the analytical and development pipeline," notes Andrew Notohamiprodjo, head of data science at Delos. "Using Google Earth Engine for risk modeling and pre-processing has reduced data preparation time by 80% compared to outsourcing, and the curated data library has significantly accelerated research and product development." To produce the risk models that underlie Delos's underwriting workflows, this data is run through scripts written using Google Colab notebooks, a platform that allows developers to write and execute code directly in their web browser. It then flows into pipelines built on the Vertex AI Platform for model training and deployment.
"The Google Earth Engine data catalog offers an easily accessible repository of uniformly formatted landscape and weather data, eliminating the burden of manual data cleansing and ensuring data consistency throughout the
Delos uses Google Earth Engine and AI-driven geospatial modeling to accurately predict wildfire risk
Delos's software, powered by Google Earth Engine, uses that data to identify which areas deemed uninsurable by other companies are actually at a lower risk of wildfire, and offer affordable insurance policies to homeowne
Five-minute quote-to-buy underwriting workflow powered by Google Maps Platform
Using Google Earth Engine and AI-driven geospatial modeling to accurately predict wildfire risk enables Delos to offer affordable insurance to California homeowners deemed uninsurable by other carriers.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Google Cloud Sample Modified · Oct 9, 2026 Policybazaar Motor insurance options in India can be complex, which is why Policybazaar built its market-leading reputation by making the consumer journey entirely transparent and accessible. The digital platform empowers millions… Achieved 97% issuance within five minutes in PAYD using Gemini models Cloud Storage · Gemini 2.5 Flash · Gemini 2.5 Flash-Lite Google Cloud Sample Archived · Sep 17, 2026 Home Credit Bank Home Credit Bank needed a safer enterprise AI environment that non-technical teams could use to build banking agents quickly without creating data-leak risk or relying on a long custom-development cycle for each new use… Achieved 100% migration to a secure AI environment Agent Designer · Gemini Enterprise Google Cloud Sample Modified · Aug 28, 2026 PvX Partners The PvX team needed a way to ingest and normalize the data into a single model while staying aligned with strict compliance standards from the Monetary Authority of Singapore (MAS). The platform also had to scale to… Committed $1B+ of financing to gaming and consumer app companies BigQuery · Cloud Run · Cloud SQL 