LexisNexis Risk Solutions has launched an AI-driven property risk model aimed at helping US home insurers improve underwriting accuracy as claims costs continue to rise.
The new platform, LexisNexis Location Intelligence for Home, uses neural network modelling and industry-wide claims data to predict property-level insurance risk across both weather and non-weather-related events.
The tool has been integrated into LexisNexis Smart Selection, the company’s automated underwriting data platform used by insurers to identify and assess higher-risk properties during both new business and renewal processes.
Home insurers are facing mounting pressure from rising repair costs, increasing catastrophe losses and tighter underwriting margins. LexisNexis argues that traditional property assessment methods, which often rely heavily on exterior inspections and historical claims data, no longer provide a complete picture of risk.
The new model combines location-based insights with historical loss patterns to generate predictive risk scores across six key perils: hail, wind, weather-related water damage, non-weather-related water damage, freeze damage and collapse or falling object risk. LexisNexis said properties with the highest Location Intelligence scores are 20 times more likely to generate a claim than those with the lowest scores.
The platform also incorporates optional roof condition grading and embeds risk insights directly into underwriting workflows, allowing insurers to apply assessments more consistently across teams.
LexisNexis pointed to non-weather water damage as a growing challenge for insurers. In 2025, non-weather water claims accounted for 24% of all home insurance claims, compared with 4% linked to weather-related water losses.
The technology also gives insurers portfolio-level visibility across their books of business, helping carriers manage concentrations of risk and refine underwriting decisions over time.
LexisNexis Risk Solutions vice president of home insurance George Hosfield said, “Rising loss costs and shifting risk patterns are making it harder for home insurers to rely on traditional underwriting approaches alone.
“By bringing Location Intelligence into Smart Selection, we’re giving home insurers a more consistent way to assess personal property risk using deeper, location-based insights within the workflows they already use. Understanding where claims are more likely to occur also provides carriers an opportunity to partner with consumers to mitigate those risks in advance. We’ll continue to invest in these capabilities, so insurers can keep pace with changing risk and make more informed decisions over time.”
Datos Insights senior principal Meredith Barnes-Cook said, “Traditional property risk models were built around the signals that were easiest to get, not necessarily the ones most predictive of loss.
“Non-weather water alone represents nearly a quarter of all home claims — a risk driver that exterior inspections and weather overlays routinely miss. The next generation of underwriting tools needs to close that gap at the individual property level.”
LexisNexis previously rolled out Location Intelligence capabilities within the commercial insurance market and plans to file the predictive model across additional US states in the coming months for use in home insurance underwriting and rating workflows.
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Facts Only
* LexisNexis Risk Solutions launched an AI-driven property risk model.
* The platform is named Location Intelligence for Home.
* The model uses neural network modeling and industry-wide claims data.
* The model predicts property-level insurance risk across weather and non-weather-related events.
* The tool is integrated into LexisNexis Smart Selection.
* The system identifies and assesses higher-risk properties during new business and renewal processes.
* The model generates predictive risk scores across six key perils: hail, wind, weather-related water damage, non-weather-related water damage, freeze damage, and collapse or falling object risk.
* Properties with the highest Location Intelligence scores are estimated to be 20 times more likely to generate a claim than those with the lowest scores.
* Non-weather water claims accounted for 24% of all home insurance claims in 2025, compared to 4% linked to weather-related water losses.
* LexisNexis plans to file the predictive model across additional US states in the coming months.
Executive Summary
Full Take
The narrative positions a large technology provider offering a solution to a severe market problem—rising loss costs and the inadequacy of traditional risk assessment methods. The core pattern is the reframing of risk from an observational, static process (exterior inspections, historical claims) to a predictive, dynamic one (location intelligence, neural networks). This shift serves to justify the integration of a proprietary system, creating an authoritative framework where only the new method provides a "complete picture" of risk.
The implication is that risk is not just a matter of historical fact but a variable location-based probability, and access to this predictive capability—via the platform—is necessary for effective decision-making. The prominence given to non-weather water damage (24% of claims) establishes a clear focus, suggesting that the solution targets a systemic blind spot ignored by legacy methods. This is a strategic move to frame traditional underwriting methods not as obsolete, but as incomplete, thereby creating a necessity for the new technology.
The skepticism arises when evaluating whether the integration of a proprietary model, while offering consistency, genuinely addresses the complexity of risk or simply provides a new layer of managed risk. The transition from external observation to internal, location-based prediction raises questions about where the power resides: whether the data and modeling capabilities create a genuinely equitable assessment tool, or if they merely optimize the efficiency of existing risk concentration patterns.
Sentinel — Human
This text reads as professionally sourced business journalism, effectively integrating technical developments with expert commentary to explain a complex industry shift.