InsurTech and geospatial insurance analytics: what P&C carriers need to know
InsurTech is changing P&C underwriting, claims and pricing. See how geospatial property intelligence from Nearmap powers it.
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InsurTech, also spelled insuretech, is the use of technology such as artificial intelligence, geospatial data, and automation to change how insurers underwrite risk, price policies, and settle claims. Property and casualty (P&C) carriers adopt insurtech tools to replace manual data collection and on-site inspection with structured property information that models and underwriters can use directly.
This article defines the insurtech ecosystem, explains where Nearmap sits among insurtech data providers, and describes how geospatial AI produces underwriting data. It then documents how two US insurers applied that data.
What is InsurTech?
Defining the insurtech ecosystem
The insurtech ecosystem consists of the carriers, managing general agents, data providers, platform vendors, and start-ups that supply technology across the policy lifecycle, from quoting and underwriting through claims and renewal. Data providers hold a distinct position in that ecosystem because every other tool, from rating engines to claims estimators, produces better output when the property information it receives is current and accurate.
InsurTech vs. traditional insurance technology
Traditional insurance technology centers on core systems of record, such as policy administration and claims platforms, which store and process information the insurer already holds. Insurtech adds data and analytics layers that bring new information into those systems, for example AI-derived property attributes delivered through APIs and direct integrations, so that decisions draw on the current condition of each property.
Why ‘insuretech’ and ‘insurtech’ are the same market
Insuretech and insurtech are two spellings of one term, a contraction of insurance and technology, and both describe the same market of technology products and providers serving insurers. Industry publications and search queries use the two spellings interchangeably.
Nearmap in the InsurTech ecosystem
Nearmap supplies insurtech data to P&C carriers in the United States, Canada, Australia, and New Zealand, pairing its own aerial capture program with AI-derived property attributes delivered through Betterview, APIs, and core system integrations.
Betterview: AI property intelligence for P&C carriers
Betterview is a property intelligence and risk management platform for P&C insurers, and it scores each property from aerial imagery inside the carrier underwriting workflow. The platform returns more than 130 AI property attributes, including the Roof Spotlight Index, a roof condition score that reflects staining, ponding, missing shingles, and other visual roof damage.
Underwriters apply automated flagging and continuous monitoring to the same property record, and Partner Connect adds third-party data from outside providers.
Guidewire integration: property data in PolicyCenter
In October 2025, Nearmap launched the Nearmap Accelerator for the Betterview Platform, an Extension that places the Betterview interface inside Guidewire PolicyCenter. Betterview property flags map automatically to underwriting issues in the risk analysis tab, and property report PDFs upload to the document portal in the policy file, which removes the need for underwriters to switch between systems. Betterview also integrates with Duck Creek Policy.
Portfolio Intelligence for risk and analytics teams
Portfolio Intelligence is a pre-built tool in the Betterview platform that aggregates property attributes and risk scores across a carrier’s policies in force and displays them on color-coded maps. Analytics teams filter the portfolio by roof condition, third-party data, and custom risk flags, locate geographic concentrations of poor roofs and high-peril properties, and drill down to the individual property.
The tool delivers portfolio-level risk segmentation without manual aggregation of data from several sources.
Geospatial data as the foundation of insurance analytics
What is GeoAI?
GeoAI, short for geospatial artificial intelligence, applies artificial intelligence and machine learning to geospatial data such as aerial imagery to extract information at scale. Nearmap GeoAI detects more than 130 features from aerial imagery, including roof materials, solar panels, and surface permeability, which converts imagery into structured data that models can read. A fuller explanation is available in the Nearmap article: What is GeoAI?
How aerial imagery powers predictive underwriting models
Predictive underwriting models need property-level inputs that are current, consistent, and available at quote, and aerial imagery supplies them without a site visit. Nearmap captures imagery at 1.57-2.95 in / 4-7.5 cm or better ground sample distance (GSD), refreshed up to three times a year in the United States, and AI converts each capture into attributes that enter a model as features.
Nearmap holds more than 60 ready-to-use rate filings across 37 states, with predictive lift documented for wildfire, hurricane, hail, and wind.
AI-derived property attributes: roof, vegetation and condition scores
Roof Age Gen2 estimates roof age to within one year of the replacement date with 95% accuracy, and Roof Condition scores describe the visible state of the roof surface. Vegetation attributes and defensible space measures describe the fuel around a structure, which bears on wildfire exposure.
Together these attributes give a carrier a property-level view of risk that updates with each new capture.
Insurance underwriting software: connecting data to decisions
Insurance underwriting software uses property data at quote, at new business review, and at renewal, and the value of that data depends on whether it reaches the underwriter inside the system where the decision is made. Applying machine learning to those inputs is covered under AI underwriting.
Risk segmentation with property-level geospatial data
Risk segmentation sorts a book of business into groups with different expected loss, and property-level geospatial data gives each group a measured basis such as roof condition band. Automated flagging applies carrier-defined rules to those scores, which routes high-risk properties to inspection or underwriter review and allows lower-risk properties to be triaged without an inspection. Insurance analytics software tracks the resulting segments over time as new imagery arrives.
Actuarial tools built on geospatial intelligence
Actuarial and data science teams use the same attributes to test rating variables, validate models, and support rate filings, because the data is explainable and auditable. Nearmap supports these teams with an insurance data science solution and delivers attributes by API, while Portfolio Intelligence adds a view of risk concentrations across the book.
Case studies: InsurTech in practice
Ohio Mutual Insurance Group: 62% reduction in inspection costs
Ohio Mutual Insurance Group, founded in 1901 and distributing through more than 500 independent agencies, reduced inspection costs by 62% within two years of adopting Betterview. The carrier flagged its book on roof condition attributes, prioritized the flagged risks, and used the scores to triage risk without ordering inspections. After two years, nearly 47% of its property book sits in the highest visual score decile (90 to 100).
Arden Insurance Services: underwriting at scale
Arden Insurance Services is a managing general agency (MGA) that insures residential condominium associations in the Western United States and certain Midwest states, and it relied on post-bind physical inspections to confirm underwriting decisions while the business grew quickly. Within several months of adopting Betterview, Arden improved its loss ratio by 5.5% and cut average underwriting time per property by 9%, and the underwriting team handled three or more additional submissions per week.