Table of Contents
- Executive Summary: Key Trends and Market Outlook (2025–2029)
- Regulatory Landscape and Industry Standards in Quake Analytics
- Core Technologies: AI, Machine Learning, and IoT in Seismic Risk
- Market Sizing, Segmentation, and Growth Forecasts Through 2029
- Competitive Landscape: Leading Vendors and Emerging Innovators
- Integration with Insurance and Financial Services
- Deployment in Construction, Urban Planning, and Infrastructure
- Case Studies: Real-World Deployments and Outcomes
- Challenges, Barriers, and Data Security Concerns
- Future Outlook: Next-Gen Tech, Investment, and Industry Impact
- Sources & References
Executive Summary: Key Trends and Market Outlook (2025–2029)
Quake risk analytics is undergoing rapid transformation as advances in seismology, data science, and high-performance computing converge to deliver more granular, actionable insights to stakeholders across the insurance, infrastructure, and public safety sectors. By 2025, this market is characterized by increased integration of real-time sensor networks, improved modeling techniques, and the proliferation of open-access seismic data, which together enhance risk quantification and mitigation strategies.
Recent years have seen national and regional governments, as well as private actors, expand the deployment of dense seismic sensor arrays and Internet of Things (IoT)-enabled structural monitoring, providing high-frequency ground motion data and enabling more accurate loss estimation. For example, the United States Geological Survey’s ShakeAlert system now covers the entire U.S. West Coast, offering real-time earthquake early warnings to millions (United States Geological Survey). In Japan, the Japan Meteorological Agency continues to expand and refine its nationwide earthquake and tsunami observation network, integrating real-time analytics with public alerting systems (Japan Meteorological Agency).
Commercial risk model providers such as RMS and Verisk are leveraging high-resolution geospatial data and machine learning to update probabilistic catastrophe models, which are increasingly tailored to local building codes, soil types, and exposure maps. These analytics are central to property and casualty insurance pricing and portfolio management, as well as to public sector resilience initiatives.
Looking ahead to 2029, the sector is poised for further expansion as new satellite-based geodesy missions—such as those supported by the European Space Agency’s Copernicus program (European Space Agency)—deliver continuous ground deformation measurements. These data streams are expected to feed directly into risk models, reducing uncertainties and enabling dynamic risk assessment. Additionally, open data initiatives from groups such as the Global Earthquake Model Foundation are catalyzing innovation in risk analytics, democratizing access to hazard and vulnerability data worldwide.
In summary, quake risk analytics is shifting toward higher temporal and spatial resolution, real-time responsiveness, and broader data accessibility. These trends, coupled with the integration of artificial intelligence, are expected to drive significant market growth and improved risk mitigation capabilities over the next five years.
Regulatory Landscape and Industry Standards in Quake Analytics
The regulatory landscape and industry standards for quake risk analytics are evolving rapidly, driven by both technological advances and a heightened recognition of seismic risk in urban planning, insurance, and infrastructure resilience. In 2025, updated frameworks and requirements are shaping how organizations collect, process, and leverage seismic data to assess risk and inform mitigation strategies.
A cornerstone of this regulatory environment is the work of the U.S. Geological Survey, which continues to refine the National Seismic Hazard Model (NSHM). The most recent NSHM updates, released in 2023, integrate new geophysical data and earthquake catalogues to produce more granular hazard maps. These maps underpin national building codes and are referenced in the standards set by the Federal Emergency Management Agency (FEMA), which in turn inform state and local regulations regarding seismic design and retrofitting.
The private sector is responding to these regulatory shifts by enhancing analytics platforms and risk models. Companies such as RMS and AIR Worldwide (now part of Verisk) are updating their quake risk models to align with the latest USGS data and regional regulatory changes. These models are critical to insurers and reinsurers, who must now comply with stricter solvency and disclosure requirements under frameworks like the National Association of Insurance Commissioners (NAIC) Own Risk and Solvency Assessment (ORSA).
Globally, standards from organizations such as the International Organization for Standardization (ISO) are gaining importance. ISO 22327:2018, which addresses earthquake early warning systems, is being adopted by more countries as part of broader disaster risk reduction strategies. In Asia-Pacific, Japan’s Meteorological Agency and New Zealand’s GeoNet provide real-time data streams that are increasingly integrated into multinational risk analytics platforms, reflecting a trend toward harmonized, cross-border data standards.
- Integration of real-time sensor data—often mandated by local authorities—is accelerating, with smart city projects requiring adherence to open data protocols and interoperability standards set by government and industry bodies.
- Public-private partnerships are emerging, exemplified by collaborations between the USGS and commercial analytics providers to enhance ShakeAlert earthquake early warning system coverage and reliability.
- Looking ahead, regulatory attention is shifting toward climate-related seismic risks, such as induced seismicity linked to infrastructure and energy development, prompting calls for expanded monitoring requirements and adaptive standards.
The outlook for the next few years is one of tighter regulatory integration, more dynamic data-sharing frameworks, and a move toward global harmonization of quake risk analytics standards—setting new expectations for transparency, interoperability, and resilience across industries.
Core Technologies: AI, Machine Learning, and IoT in Seismic Risk
Quake risk analytics is undergoing a transformative shift as core technologies—specifically AI, machine learning (ML), and IoT—become deeply integrated into seismic risk assessment and mitigation strategies. As 2025 unfolds, stakeholders across insurance, infrastructure, and public safety increasingly rely on these innovations to enhance predictive accuracy, response speed, and resilience planning.
AI-based models now process vast datasets from seismic networks, satellite imagery, and structural sensors in real time. For example, RMS uses advanced analytics platforms that combine ML algorithms with historical and real-time seismic data to deliver more granular risk models, influencing underwriting and catastrophe bonds in earthquake-prone regions. These tools allow insurers and governments to simulate damage scenarios and financial impacts with unprecedented precision.
Machine learning advances are also enabling early warning systems to identify complex seismic patterns that traditional methods might overlook. U.S. Geological Survey (USGS) operates the ShakeAlert system, which leverages ML techniques to rapidly analyze data from thousands of ground sensors, delivering alerts seconds before damaging shaking occurs. The continuous improvement of these algorithms, scheduled through 2025 and beyond, aims to further reduce false alarms and increase lead times, helping organizations and individuals take rapid protective actions.
Meanwhile, IoT is redefining data collection and risk communication. Companies like SeismicAI deploy dense networks of low-cost, connected accelerometers and geophones across urban environments, offering hyper-localized seismic readings. These IoT devices feed high-frequency data streams into central analytics engines, enabling dynamic risk profiling for critical infrastructure such as bridges, hospitals, and energy facilities. As urban IoT deployments expand into 2026, they are expected to provide actionable insights not just for emergency response but also for ongoing maintenance and retrofitting strategies.
Looking forward, the convergence of AI, ML, and IoT will likely drive further advances in quake risk analytics. Enhanced data fusion from satellite, drone, and sensor sources will improve the granularity and reliability of risk maps. Moreover, collaborative initiatives—such as those led by Global Earthquake Model Foundation—are promoting open standards and shared analytics platforms to ensure broader access and interoperability. This collective momentum is set to redefine risk modeling and resilience planning in earthquake-prone regions throughout 2025 and in the years to follow.
Market Sizing, Segmentation, and Growth Forecasts Through 2029
The global market for quake risk analytics is experiencing robust growth, propelled by increasing seismic activity awareness, advances in geospatial data processing, and the pressing need for resilience in urban infrastructure. As of 2025, the sector encompasses a broad range of solutions—ranging from probabilistic seismic hazard models to real-time risk assessment platforms—serving industries such as insurance, construction, utilities, and government entities.
In terms of market sizing, the quake risk analytics domain is segmented by deployment (cloud-based vs. on-premises), end-user (insurance and reinsurance, civil engineering, government, utilities, and real estate), and geography. Leading providers, such as RMS (a Moody’s Analytics company) and CoreLogic, are reporting sustained demand for high-resolution risk models, particularly in seismically active regions of North America, East Asia, and the Mediterranean. For example, RMS notes a growing trend among insurers toward adopting next-generation analytics to refine risk selection and pricing, with a focus on tail-risk events and secondary perils such as liquefaction and landslide.
The segmentation by end-use verticals shows insurance and reinsurance firms as the largest adopters, leveraging quake analytics for exposure management, catastrophe bond structuring, and regulatory compliance. Civil engineering and real estate sectors are increasingly integrating analytics for site selection, retrofitting decisions, and asset valuation. Governments and utilities utilize these tools for disaster preparedness and infrastructure planning, with agencies like the U.S. Geological Survey (USGS) offering open-access seismic hazard data that underpins many commercial models.
Growth forecasts through 2029 remain strong, driven by technological advancements such as machine learning-based hazard models, integration with IoT sensor data, and the expansion of earthquake early warning systems. Companies like K-NET in Japan and GEM Foundation globally are contributing to the granularity and reliability of datasets, facilitating new analytics applications. Moreover, mandatory risk disclosure regulations and increasing climate-induced disaster volatility are expected to further accelerate market adoption.
Overall, the market for quake risk analytics is forecasted to grow at a high single-digit to low double-digit CAGR through 2029, with the Asia-Pacific region likely to see the fastest expansion due to urbanization and high seismic risk. Continued investment in data infrastructure and model accuracy, as evidenced by ongoing updates from RMS and CoreLogic, will be critical to capturing this growth.
Competitive Landscape: Leading Vendors and Emerging Innovators
The competitive landscape of quake risk analytics in 2025 is shaped by rapid advancements in geospatial data processing, AI-driven predictive modeling, and cloud-based platforms for risk assessment. Established industry leaders are consolidating their positions with expanded offerings, while a new wave of technology-driven startups is introducing innovative approaches to seismic risk evaluation and mitigation.
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Leading Vendors:
Major global players such as RMS (Risk Management Solutions) and AIR Worldwide continue to dominate the market. RMS’s Earthquake Models are widely adopted by insurers and reinsurers for underwriting and portfolio management, leveraging a combination of historical seismicity, fault data, and exposure analytics. AIR Worldwide, a Verisk business, has recently enhanced its Earthquake Model Suite to incorporate high-resolution soil and site data, reflecting a trend toward localized risk differentiation. -
Emerging Innovators:
Startups and tech-driven firms are disrupting the sector with new methodologies. One Concern employs machine learning and digital twin technologies to provide real-time quake impact forecasts for businesses and municipalities, enabling dynamic risk management and resilience planning. Kairospace is integrating satellite-based interferometry and IoT sensor networks to deliver granular, up-to-the-minute risk assessments, targeting infrastructure operators and urban planners. -
Collaborative Initiatives:
Collaboration between vendors and public sector bodies is intensifying. The U.S. Geological Survey (USGS) Earthquake Hazards Program partners with analytics vendors to provide authoritative seismic hazard data, which is increasingly accessed through open APIs and integrated into commercial risk platforms. In Japan, the National Research Institute for Earth Science and Disaster Resilience (NIED) collaborates with both insurers and tech firms, facilitating the integration of real-time ground motion data into analytical tools. -
Outlook (2025–2028):
Over the next few years, the sector is expected to see continued convergence of AI, remote sensing, and real-time monitoring. Leaders are investing in cloud-native architectures for scalability and interoperability, while innovators focus on hyper-local modeling and rapid response analytics. Competitive differentiation will increasingly hinge on the ability to translate complex seismic data into actionable business intelligence for risk transfer and resilience.
Integration with Insurance and Financial Services
The integration of quake risk analytics into insurance and financial services is accelerating rapidly as advancements in data science and real-time monitoring reshape the industry landscape. In 2025, insurers and reinsurers are leveraging sophisticated earthquake modeling platforms to better assess, price, and transfer seismic risk. These platforms combine high-resolution seismic hazard data, structural vulnerability models, and financial exposure information to deliver granular risk insights at the property, portfolio, and regional levels.
A noteworthy development is the increased adoption of catastrophe risk models and analytics by major insurance providers. For instance, Swiss Re and Munich Re have expanded their internal analytics capabilities, integrating proprietary earthquake models and global event databases to support underwriting decisions and capital allocation. These models simulate thousands of possible seismic events, accounting for local soil conditions, building codes, and recent seismicity, thereby enabling more accurate premium calculations and risk-adjusted pricing.
In parallel, parametric insurance products—where payouts are triggered by pre-defined seismic parameters rather than actual loss assessments—are gaining traction. Technology providers such as PERILS AG and AXA XL offer parametric solutions that use real-time ground motion data from global seismic networks to ensure rapid claims settlement and improved transparency. These products are particularly relevant for businesses and governments seeking immediate liquidity following major earthquakes.
Financial markets are also witnessing growth in insurance-linked securities (ILS) tied to earthquake risk. Catastrophe bonds and other ILS instruments, backed by quake risk analytics, are increasingly used as alternative risk transfer mechanisms. For example, Guy Carpenter provides advanced earthquake risk analytics that support ILS structuring and investor due diligence, helping to bridge the gap between insurance and capital markets.
Looking ahead, the integration of artificial intelligence and machine learning with quake risk analytics is expected to further enhance predictive accuracy and operational efficiency. The continued expansion of open data initiatives—such as those led by the Global Earthquake Model Foundation—will facilitate broader adoption, enabling insurers and financial institutions to refine their risk management strategies, meet regulatory requirements, and deliver more resilient products in the face of evolving seismic hazards.
Deployment in Construction, Urban Planning, and Infrastructure
Quake risk analytics is playing an increasingly central role in the deployment of construction, urban planning, and infrastructure projects in 2025 and the coming years. As seismic activity continues to pose significant risks to densely populated and rapidly urbanizing regions, advances in data analytics, sensor technologies, and simulation models are informing more resilient strategies for both new developments and retrofits.
In 2025, leading construction and engineering firms are leveraging high-resolution seismic hazard models and real-time geospatial data to identify site-specific vulnerabilities before breaking ground. For example, Arup integrates risk analytics into digital twin environments, allowing urban planners to visualize and simulate earthquake scenarios, optimize building placement, and design infrastructure that meets or exceeds current codes. Similarly, Jacobs incorporates seismic risk modeling into its infrastructure projects, particularly in high-risk zones such as the Pacific Rim and South Asia.
Municipalities and urban planners are increasingly adopting data-driven frameworks for resilience assessment. The deployment of dense seismic sensor networks—enabled by organizations like United States Geological Survey (USGS) and GNS Science—feeds real-time ground motion and microzonation data into urban risk models. This integration supports zoning decisions, informs retrofitting priorities, and helps evaluate the cost-benefit of mitigation measures at both the district and city scales.
The adoption of open-data platforms and standardized risk scoring systems is also accelerating collaboration between public authorities and private developers. Initiatives such as the Global Earthquake Model (GEM) Foundation provide accessible risk assessment tools and global datasets, enabling stakeholders to benchmark vulnerabilities and coordinate emergency preparedness across jurisdictions.
Looking ahead, the outlook for quake risk analytics is shaped by ongoing innovation in artificial intelligence and machine learning. These technologies are enhancing the predictive accuracy of risk models, supporting the automation of structural health monitoring, and enabling dynamic response strategies. For example, Fugro is deploying AI-enhanced analytics to monitor the integrity of critical infrastructure such as bridges and dams in seismic zones, providing early warnings and maintenance recommendations.
As urbanization intensifies and climate change alters hazard profiles, the integration of advanced quake risk analytics into construction and planning will be essential for safeguarding lives, assets, and the continuity of urban economies in 2025 and beyond.
Case Studies: Real-World Deployments and Outcomes
In 2025, quake risk analytics continues to evolve rapidly, with real-world deployments demonstrating the tangible benefits of advanced data analytics, sensor networks, and AI-driven modeling for seismic risk management. Several major case studies illustrate how organizations and municipalities are leveraging these technologies to mitigate earthquake risks and enhance resilience.
One notable deployment is in the city of Los Angeles, where the City of Los Angeles has partnered with technology providers to enhance its early warning and risk analytics infrastructure. Leveraging dense networks of ground sensors and real-time data feeds, the city can now provide automated alerts and predictive risk maps to critical infrastructure operators and emergency services. These analytics systems integrate with the U.S. Geological Survey’s ShakeAlert platform, offering improved accuracy in forecasting potential ground shaking and estimating likely damage scenarios for specific neighborhoods. This has enabled the city to optimize emergency response protocols and target retrofitting efforts for vulnerable structures.
In Japan, NTT Communications has deployed a comprehensive quake risk analytics platform for several municipalities and industrial clients. Utilizing its IoT sensor network, NTT combines real-time seismic data with machine learning algorithms to assess infrastructure vulnerability and simulate quake impacts on supply chain operations. In 2024, this system successfully supported disaster planning for the Tokyo metropolitan area by identifying critical nodes in transportation and utility networks, enabling prioritized resource allocation and minimizing disruptions during seismic events.
The insurance sector has also seen significant advancements with the adoption of advanced quake risk analytics. Munich Re, a global reinsurer, has integrated high-resolution seismic hazard models and exposure data into its underwriting and risk assessment processes. By 2025, these tools have enabled more precise pricing of earthquake insurance products and facilitated proactive risk reduction strategies for clients in high-risk regions. Munich Re’s analytics platforms incorporate both historical data and real-time sensor input to generate probabilistic loss scenarios, informing both customers and public sector partners.
Looking forward, the deployment of 5G connectivity and edge computing is expected to further accelerate quake risk analytics capabilities. Initiatives from Ericsson and other network infrastructure providers are enabling faster data transmission from distributed seismic sensors, promising near-instantaneous risk assessments and automated system responses in the coming years. This integration of communication and analytics technologies will likely lead to broader adoption across urban centers worldwide, enhancing resilience in the face of growing seismic risks.
Challenges, Barriers, and Data Security Concerns
Quake risk analytics faces a complex array of challenges and barriers in 2025, particularly as the industry seeks to integrate newer data streams, apply advanced modeling techniques, and meet rising expectations for real-time, actionable insights. One of the foremost challenges is data heterogeneity and quality. Seismic risk models depend on inputs from diverse sources—historical earthquake catalogs, satellite remote sensing, geodetic networks, and structural vulnerability databases. Ensuring consistency, accuracy, and completeness across these datasets remains a persistent hurdle, especially in regions where historical seismicity is poorly documented or infrastructure data is outdated. For instance, organizations such as the United States Geological Survey continue to highlight the gaps in global seismic monitoring, which can limit model reliability in emerging markets and remote areas.
Another barrier is computational intensity and scalability. High-resolution risk models, which are now increasingly feasible due to advances in cloud computing and AI, require vast processing power and robust data pipelines. This can strain the IT resources of smaller insurers, municipalities, and engineering firms, potentially limiting access to state-of-the-art analytics. To address these issues, companies like RMS (Risk Management Solutions) and AIR Worldwide are investing in cloud-native platforms and API-driven services, but interoperability and cross-platform integration remain ongoing challenges for the sector.
Data security and privacy concerns are also at the forefront. The integration of sensitive property, infrastructure, and personal information into risk models raises significant cybersecurity and regulatory issues. Ensuring compliance with data protection frameworks—such as GDPR in Europe or CCPA in California—necessitates rigorous encryption, access controls, and auditability of all workflows. The International Organization for Standardization has issued updated standards (e.g., ISO/IEC 27001:2022) guiding information security management, and leading analytics providers are increasingly seeking such certifications to assure clients of their data stewardship.
Looking ahead to the next few years, the sector will need to contend with the growing risks posed by climate-driven secondary hazards (e.g., landslides, tsunamis triggered by earthquakes), integration of real-time IoT sensors, and the expectation for transparent model explainability. Addressing these challenges will rely not only on technical innovation but also on establishing stronger public-private partnerships, open-data initiatives, and shared security frameworks—trends already being advocated by organizations such as the Global Earthquake Model Foundation.
Future Outlook: Next-Gen Tech, Investment, and Industry Impact
The field of quake risk analytics is rapidly advancing as next-generation technologies, increased investment, and cross-sector collaboration redefine how seismic risks are understood and managed. Looking ahead to 2025 and the following years, several key trends are set to shape the industry’s trajectory.
First, the integration of machine learning and artificial intelligence is accelerating the ability to predict, model, and assess earthquake hazards. Companies such as RMS and AIR Worldwide are investing in AI-driven platforms that synthesize vast datasets—ranging from historical quake records to real-time sensor feeds—to generate more dynamic and granular risk maps. This shift promises not only improved accuracy but also more actionable insights for insurers, governments, and infrastructure operators.
Second, the deployment of high-density sensor networks is transforming ground-level data collection. Initiatives like the U.S. Geological Survey’s Earthquake Hazards Program are expanding seismic monitoring infrastructure, leveraging IoT-enabled sensors to deliver real-time ground motion data. Such enhancements are crucial for early warning systems, rapid loss estimation, and adaptive risk management strategies, particularly in urban centers and critical infrastructure corridors.
On the investment front, the insurance sector continues to drive demand for advanced quake analytics. Reinsurers and catastrophe bond issuers increasingly rely on robust, transparent modeling to price risk and structure products. For example, Munich Re has underscored the need for expanding analytics capabilities to address rising exposures from urbanization and climate-linked risk factors. Startups and established players in the risk modeling ecosystem are responding with cloud-based, API-driven solutions that foster greater interoperability and data sharing.
Looking forward, regulatory and industry standards are expected to evolve in tandem with technological advances. Bodies like the Global Earthquake Model Foundation are working to harmonize risk modeling frameworks, promoting open data and transparent methodologies. This trend will likely accelerate as stakeholders demand greater confidence in models underpinning capital allocation, urban planning, and disaster preparedness.
In summary, the next few years will see quake risk analytics become more predictive, automated, and accessible, driven by investments in AI, IoT, and data standardization. These advances are poised to deliver significant benefits across insurance, infrastructure, and public safety, underpinning more resilient communities amid increasing seismic risk.
Sources & References
- Japan Meteorological Agency
- Verisk
- European Space Agency
- International Organization for Standardization
- GeoNet
- SeismicAI
- Global Earthquake Model Foundation
- CoreLogic
- K-NET
- One Concern
- National Research Institute for Earth Science and Disaster Resilience (NIED)
- Munich Re
- Guy Carpenter
- Arup
- Jacobs
- GNS Science
- Fugro
- City of Los Angeles
