{"id":236982,"date":"2019-05-22T05:23:25","date_gmt":"2019-05-22T12:23:25","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&#038;p=236982"},"modified":"2024-05-10T06:39:48","modified_gmt":"2024-05-10T13:39:48","slug":"insurers-assess-climate-change","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/insurers-assess-climate-change","title":{"rendered":"Changing Hazards Prompt Insurers to Rethink the Location of Risk"},"author":501,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","castos_file_data":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[455201,369832,1001],"tags":[1641,281,280372,356482],"department":[488822],"wherenext-category":[],"industry":[],"class_list":["post-236982","wherenext","type-wherenext","status-publish","format-standard","hentry","category-a-climate-of-change","category-business-risk","category-commercial","tag-insurance","tag-location-intelligence","tag-natural-disasters","tag-risk-scoring","department-sustainability-risk"],"acf":{"short_description":"To ensure their risk assessment reflects risks on the ground, insurers and reinsurers are increasingly relying on location-based risk analysis.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"Recent headlines about climate change make it difficult to bury one\u2019s head in the (rapidly receding) sand.\r\n\r\nIn 2018, a total of nearly 400 natural disasters caused US$225 billion in damage worldwide. The insurance industry covered $90 billion of those losses, <a href=\"https:\/\/www.insurancejournal.com\/news\/international\/2019\/01\/22\/515420.htm\">according to reinsurer Aon<\/a>. For historical context, consider data from the Brussels-based Centre for Research on the Epidemiology of Disasters: In the last half century, the number of <a href=\"https:\/\/ourworldindata.org\/natural-disasters\">natural disasters worldwide has risen more than 450 percent<\/a>."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"<strong>Article snapshot:<\/strong> The insurance industry is evolving from generalizations about location-based risk to sophisticated risk scoring based on GIS-powered location intelligence.","snippet":""},{"acf_fc_layout":"content","content":"Concern over the financial effects of weather events has rippled through other industries, including financial services. In a post titled \u201c<a href=\"https:\/\/corpgov.law.harvard.edu\/2019\/04\/08\/hedging-climate-news\/\">Hedging Climate News<\/a>\u201d on the Harvard Law School website, several finance professors outline a strategy for hedging against <a href=\"https:\/\/www.esri.com\/en-us\/about\/climate-action\/overview\/climate-risk-finance-insurance\">climate-based risk.<\/a>\r\n\r\nViewed over a longer horizon, the numbers are staggering. Total global economic losses from natural disasters between 2005 and 2015 were more than $1.3 trillion, with <a href=\"https:\/\/www.insurancejournal.com\/news\/national\/2018\/08\/23\/499027.htm\">direct losses in the range of $2.5 trillion since 2000<\/a>.\r\n\r\nWhile scientists work to <a href=\"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/nexttech-climate-change\/\">assess the fate of the planet<\/a>, insurance companies are dealing with the immediate and long-term consequences and costs of climate change. Increasingly, insurers of all types are using location intelligence to get a more complete picture of risk at each property, across supply chains, and across portfolios.\r\n\r\nThe reality for insurers is that understanding risks specific to location matters greatly, and is key to reliable risk assessment and pricing models. Without accounting for the context of location, insurance companies put themselves at risk. And as the insurers of last resort, reinsurers face even greater exposure. They are the final entity in the chain of climate-change-related liability; the buck stops with them.\r\n\r\n<strong>More Transparent Risk Analysis<\/strong>\r\n\r\nAndreas Siebert, head of geospatial solutions at global reinsurer Munich Re, says the realities of climate change are making Munich Re\u2019s underwriters hungrier for data that can provide both a holistic and fine-grained picture of climate-related property risks.\r\n\r\n\u201cWe try to keep our clients insurable in the future by supporting them with not only the financial strength of Munich Re but also our knowledge and experience,\u201d Siebert says.\r\n\r\nLike other insurers, Munich Re needs highly granular data upon which to base its risk pricing, as well as greater transparency into how risk-related decisions are made throughout client supply chains. Increasingly, that means relying on <a href=\"https:\/\/www.esri.com\/en-us\/location-intelligence\">location intelligence<\/a> powered by a geographic information system (GIS), which maps, analyzes, and models risk for a property and its surrounding area."},{"acf_fc_layout":"form","form_type":"aside","form_position":"Right","form_title":"CONSULT THE ESRI BRIEF","form_desc":"Stay up to date with this biweekly email featuring thought-provoking articles on location intelligence and critical technology trends.","form_button_label":"Sign up now","form_content":"https:\/\/go.pardot.com\/l\/82202\/2017-10-12\/jw1bmb","form_tag":"low-commitment-form\/sign-up-form"},{"acf_fc_layout":"content","content":"\u201cMore transparency means a better understanding of what the industry is insuring these days,\u201d Siebert explains. \u201cWe have to learn more [about building] construction types, occupancies, the age of buildings\u2014a lot of additional data. We have to bring it together to have the full transparency when we are doing risk assessment.\u201d\r\n\r\nFor Munich Re, there is a tight linkage between business intelligence and location intelligence. \u201cWe are on our way to fully integrating both worlds,\u201d Siebert says. \u201cFor us, it means we have direct access to the operational data, which we enrich and analyze with the capabilities of geo-analytics and other dashboard methodologies.\u201d\r\n\r\nMunich Re is bringing easy-to-use tools with geospatial capabilities to its underwriting practices. Since the underwriters are not geospatial experts, the tool has to be especially user-friendly and accessible.\r\n\r\n<strong>Seeing Risk with More Precision<\/strong>\r\n\r\nUntil roughly the 1980s, the insurance industry did not think at a detailed level about the link between risk and location. That has changed dramatically. <a href=\"https:\/\/en.wikipedia.org\/wiki\/Catastrophe_modeling\">NatCat models<\/a>, for instance, now compensate for the fact that areas that weren't prone to wildfires are now hot zones.\r\n\r\nWith GIS-driven location intelligence, the risk profile is much more granular, examining questions such as, \"How has this home in this neighborhood been affected by wildfires and other risks?\" Smart maps provide an immediate and compelling illustration of that risk.\r\n\r\nSuch location intelligence helps drive \u201cpoint-based risk scoring,\u201d which gives underwriters a more complete view of a property than they have traditionally had. Insurers might take into account everything from local soil composition to the types of trees in a nearby forest (since some are more <a href=\"https:\/\/www.mnn.com\/earth-matters\/wilderness-resources\/stories\/these-trees-can-survive-forest-fire\">fire-resistant<\/a> than others), as well as age and type of property (and a long list of other factors) to arrive at a point-based risk score.\r\n\r\nLocation intelligence helps unify disparate data relating to a property and its type of risk, whether that is flood, hurricane, wildfire, hail, tornado, earthquake or other calamities. A holistic risk profile also includes threats such as terrorism and civil unrest. In fact, a small cohort of specialists\u2014companies such as CONIAS Risk Intelligence\u2014supplies multinational corporations with <a href=\"https:\/\/www.esri.com\/en-us\/arcgis\/products\/insights-for-arcgis\/overview\">GIS-driven insight<\/a> on where those risks exist, and to what degree.\r\n\r\nThe location intelligence these analysts deliver can affect multimillion-dollar decisions on how a supply chain is organized and which markets a company serves.\r\n\r\n<strong>AI-Based Risk Scoring<\/strong>\r\n\r\nUsing GIS-based location intelligence, insurers have identified risks that appear counterintuitive, but actually reveal new realities. Case in point: Many Californians are not required to have flood insurance because most homes there are not in 100-year floodplains. However, one of the greatest risks in the last five years in California is flooding.\r\n\r\nThis seems strange for a state that has been in a drought for eight years. Yet, an analysis of location-specific factors reveals the culprits: soil composition, the amount of concrete, the lack of drainage, aging infrastructure, and the type of rainfall events increasingly common to California. In short, there's more pooling of water and more flooding because the water has nowhere to go.\r\n\r\nLocation intelligence provides insurers a more nuanced and fully faceted picture of risk, rather than a simple \u201cyes\u201d or \u201cno\u201d answer to the question, \u201cIs this property in a floodplain?\u201d\r\n\r\nGIS can quickly analyze those events and produce a risk score for a specific property or a dashboard reading of green, yellow, or red. According to Siebert and others in the industry, the process of data correlation and scoring is becoming automated through <a href=\"https:\/\/www.esri.com\/en-us\/artificial-intelligence\/\">artificial intelligence<\/a> (AI)\u2014specifically machine learning (ML), which is trained to see patterns in large volumes of data. Going forward, all an underwriter will need to do is input an address, look at the score, and then only review a policy if it is automatically denied or flagged for more review.\r\n\r\nMunich Re is doing location-based risk analysis, both internally and as a service for clients. For example, its NATHAN service\u2014the Natural Hazard Assessment Network\u2014helps companies assess the risks of natural hazards around the world, from location-based individual risk through to entire portfolios of assets.\r\n\r\n\u201cWe combine data on global natural catastrophes and enable the user to upload their [property] portfolio to have a very complex and flexible location-based analysis,\u201d Siebert says. Even professionals who don\u2019t work with GIS\u2014including claims managers and underwriters\u2014can input an address and understand its risks, Siebert explains.\r\n\r\n<strong>A Wealth of Imagery <\/strong>\r\n\r\nIn the <a href=\"https:\/\/www.esri.com\/en-us\/industries\/insurance\/overview\">insurance industries<\/a> and across the business landscape, torrents of location-specific data are pouring in from new sources, including satellite images and aerial photography. Munich Re uses some of this imagery to better monitor storms and evaluate other extreme-weather events. The high-resolution images now assist claims adjusters and engineers in calculating losses on a per-building level.\r\n\r\nThis is another instance where location intelligence and business intelligence merge.\r\n\r\n\u201cWe are working in a very tight collaboration with our data analytics teams,\u201d Siebert says. \u201cWe want to integrate geospatial tools and functions in our data lake to enrich more of our common data sets with geo-referencing and analytical capabilities. So often, geospatial data is the glue between the different entities.\u201d\r\n\r\nAs a reinsurer, as well as a primary insurer, Munich Re is keeping a keen eye on what Siebert calls \u201caccumulation control\u201d\u2014using location intelligence to identify where it has the highest potential for losses. \u201cThis is so important to steer our business,\u201d he says.\r\n\r\n<strong>Sharing Data<\/strong>\r\n\r\nOf course, location intelligence and other types of data are not just relevant within a single business\u2014even a global reinsurer like Munich Re. Insurers and reinsurers are looking to both receive and share data with their clients and partners, as well. With the rise of natural and human-made risks across the supply chain, insurers want to know who their clients\u2019 suppliers are\u2014and where they operate\u2014to accurately price risks.\r\n\r\nThough it\u2019s critical, the current state of data exchange is not where it should be, Siebert believes. \u201cWe have to improve,\u201d he says. \u201cMany of us are hoping for more open data initiatives where we have a common understanding of our risks or infrastructure.\u201d\r\n\r\nIt is still difficult to obtain data sets that cover whole markets or whole countries, Siebert says. \u201cData availability is still sometimes a nightmare.\u201d\r\n\r\nAt last year\u2019s <a href=\"https:\/\/reinsurance.org\/\">Reinsurance Association of the Americas (RAA)<\/a> event, catastrophe response managers seemed to confirm this view. Attendees said they use NOAA and United States Geologic Survey (USGS) information as a baseline and would like other data sets to help build visibility.\r\n\r\n\u201cIn many cases, major losses can occur due to the failure of one supplier in the value chain,\u201d Siebert notes. \u201cWe try to visualize it, to get a better understanding [and] link the different supplier tiers according to the product chain.\u201d To that end, Munich Re is working on creating a GIS-based solution for its clients by late this year or early 2020.\r\n\r\n\u201cWe need a better exchange of information at all levels,\u201d he says.\r\n\r\nGoing forward, such data will be increasingly important. A deeper understanding of location-based risk\u2014associated with individual buildings and supply chains alike\u2014can be an effective tool against loss from epic storms and other natural disasters triggered by climate change."}],"references":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Climate Change Ups Risk as Insurers Fight Back with Greater Visibility<\/title>\n<meta name=\"description\" content=\"As climate change sparks greater risk for buildings, businesses, and supply chains, insurers and reinsurers examine which hazards threaten which locations.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/insurers-assess-climate-change\" \/>\n<meta property=\"og:locale\" content=\"en_US\" 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