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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n2oevp5a.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gra\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Risks Alliance\">GRA<\/abbr> \u00b7 Insurance Nexus<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><p class=\"nx-scene-accent nx-es-sig\"><strong>Spatial finance<\/strong><span>Where capital meets hazard, exposure and transition<\/span><\/p><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Machine learning in insurance<\/h1><p class=\"nx-scene-lede\">Artificial intelligence and machine learning are used in insurance to read images and documents, detect fraud, triage claims, enrich exposure data and segment risk. On this platform they are treated both as methods and as a source of risk to be governed, tested and explained.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/stem\/\">Back to STEM foundation of Insurance Nexus<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Register<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Machine learning in insurance on the record.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"console\"><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Method record<\/strong><\/span><span class=\"nx-eyebrow\">STEM method<\/span><\/div><ol class=\"nx-register-rows\"><li><div><span class=\"nx-register-k\">Method<\/span><span class=\"nx-register-v\">Artificial intelligence and machine learning<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Steward<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/ai-autonomous-systems\/\">Artificial Intelligence and Autonomous Systems<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\">Insurance Nexus<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Data<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-data\">4 inputs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Checks<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-checks\">4 validation steps<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Canonical page<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">STEM Commons<\/a><\/span><\/div><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-1\" data-hue=\"climate\" aria-label=\"Application, Loss model, Data\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Application, Loss model, Data\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-1\" aria-controls=\"nx-sec-application\" aria-selected=\"true\" data-key=\"p1\">Application<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-2\" aria-controls=\"nx-sec-instrument\" aria-selected=\"false\" data-key=\"p2\">Loss model<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-3\" aria-controls=\"nx-sec-data\" aria-selected=\"false\" data-key=\"p3\">Data<\/button><\/div><section id=\"nx-sec-application\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" data-variant=\"split\" aria-labelledby=\"nx-s-2\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">What artificial intelligence and machine learning does here.<\/h2><p class=\"nx-lede\">Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. They are increasingly used for hazard detection, forecasting and decision support, and are themselves a source of risk. In insurance and catastrophe, climate and hazards and health and biosecurity, machine learning can scale detection and forecasting across interconnected <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a>. Its own failure modes must be weighed when automated outputs inform consequential decisions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M13 7 9 3 5 7l4 4\"\/><path d=\"m17 11 4 4-4 4-4-4\"\/><path d=\"m8 12 4 4 6-6-4-4Z\"\/><path d=\"m16 8 3-3\"\/><path d=\"M9 21a6 6 0 0 0-6-6\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Exposure enrichment from imagery<\/h3><p class=\"nx-p nx-mt-1\">Models extract roof type, building footprint and vegetation proximity from aerial and satellite imagery to fill gaps in exposure data, with accuracy measured against inspected properties.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m8 11 2 2 4-4\"\/><circle cx=\"11\" cy=\"11\" r=\"8\"\/><path d=\"m21 21-4.3-4.3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Fraud and claims triage<\/h3><p class=\"nx-p nx-mt-1\">Classifiers score claims for complexity or fraud indicators to route them for review, with false positive rates, appeal outcomes and group disparities monitored beside detection.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\"\/><circle cx=\"9\" cy=\"7\" r=\"4\"\/><path d=\"M22 21v-2a4 4 0 0 0-3-3.87\"\/><path d=\"M16 3.13a4 4 0 0 1 0 7.75\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Fairness testing of pricing models<\/h3><p class=\"nx-p nx-mt-1\">Pricing and underwriting models are tested for disparate outcomes and proxy effects across groups, and less discriminatory alternatives are compared for predictive performance.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-instrument\" class=\"nx-section nx-band-panel\" data-hue=\"health\" data-ns-layout=\"es-wide\" aria-labelledby=\"nx-s-3\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Loss model<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Loss exceedance, average annual loss and probable maximum loss.<\/h2><p class=\"nx-lede\">The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks every <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 catastrophe-model\" data-tk=\"Nexus risk vocabulary\" data-td=\"A model combining a stochastic hazard event set, exposure and vulnerability functions to estimate the distribution of losses from rare, severe events.\" data-ts=\"NRS-C1\" data-tr=\"draft\">catastrophe model<\/a> uses. Teaching model with stated illustrative values, not a prediction.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-in nx-fk\" id=\"nx-in-insurance-nexus-stem-ai-ml-0\" data-nxi=\"hevl\" data-nxi-id=\"hevl\" data-nxi-z=\"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\" aria-label=\"Loss exceedance, average annual loss and probable maximum loss\"><div class=\"nx-in-body\"><div class=\"nx-in-main\"><div class=\"nx-in-tabs\" data-nxi-tabs=\"true\" aria-label=\"Views of the model\" hidden><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"result\">Result<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"fan\">Uncertainty<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"tornado\">Sensitivity<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"loops\">Loops<\/button><\/div><div class=\"nx-in-stage\" id=\"nx-in-insurance-nexus-stem-ai-ml-0-stage\" data-nxi-stage=\"true\" data-view=\"result\"><svg class=\"nx-in-svg\" viewBox=\"0 0 720 340\" role=\"img\" aria-label=\"Loss exceedance curve\" data-nx-z=\"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\"><\/svg><\/div><p class=\"nx-in-tip\" data-nxi-tip=\"true\" aria-live=\"polite\"><\/p><div class=\"nx-in-bar\" data-nxi-bar=\"true\" role=\"group\" aria-label=\"Play over time\" hidden><button type=\"button\" class=\"nx-in-btn\" data-nxi-prev=\"true\" aria-label=\"Step back\">&#8249;<\/button><button type=\"button\" class=\"nx-in-btn\" data-nxi-play=\"true\" aria-pressed=\"false\">Play<\/button><button type=\"button\" class=\"nx-in-btn\" data-nxi-next=\"true\" aria-label=\"Step forward\">&#8250;<\/button><span class=\"nx-in-step\" data-nxi-step=\"true\" aria-live=\"polite\"><\/span><\/div><\/div><div class=\"nx-in-side\"><p class=\"nx-eyebrow\">Controls<\/p><div class=\"nx-in-ctl\" data-nxi-ctl=\"true\"><dl class=\"nx-in-pv\"><div><dt>Typical annual hazard intensity (location)<\/dt><dd>1.00 intensity index<\/dd><\/div><div><dt>Spread of extremes (scale)<\/dt><dd>0.80 intensity index<\/dd><\/div><div><dt>Intensity at which half the value is lost<\/dt><dd>4.00 intensity index<\/dd><\/div><div><dt>Dispersion of vulnerability<\/dt><dd>0.55<\/dd><\/div><div><dt>Share of value in the hazard footprint<\/dt><dd>40%<\/dd><\/div><div><dt>Protection standard (return period)<\/dt><dd>50 years<\/dd><\/div><\/dl><\/div><p class=\"nx-eyebrow\">Readouts<\/p><dl class=\"nx-in-out\" data-nxi-out=\"true\" aria-live=\"polite\"><div><dt>Average annual loss<\/dt><dd>0.51% of value<\/dd><\/div><div><dt>Loss at 1 in 100 years<\/dt><dd>24.5% of value<\/dd><\/div><div><dt>Loss at 1 in 250 years<\/dt><dd>28.4% of value<\/dd><\/div><div><dt>Intensity at 1 in 100 years<\/dt><dd>4.68 intensity index<\/dd><\/div><\/dl><\/div><\/div><details class=\"nx-in-more\"><summary>Numbers, methods and sources<\/summary><div class=\"nx-in-more-in\"><div class=\"nx-in-tblw\" data-nxi-table=\"true\"><table class=\"nx-in-tbl\"><thead><tr><th scope=\"col\">Return period (years)<\/th><th scope=\"col\">Intensity (intensity index)<\/th><th scope=\"col\">Loss (% of value)<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">1<\/th><td>0.09<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">2<\/th><td>1.27<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">4<\/th><td>1.91<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">7<\/th><td>2.47<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">13<\/th><td>3.00<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">24<\/th><td>3.51<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">44<\/th><td>4.02<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">82<\/th><td>4.53<\/td><td>23.55<\/td><\/tr><tr><th scope=\"row\">154<\/th><td>5.03<\/td><td>26.44<\/td><\/tr><tr><th scope=\"row\">287<\/th><td>5.53<\/td><td>28.87<\/td><\/tr><tr><th scope=\"row\">536<\/th><td>6.03<\/td><td>30.88<\/td><\/tr><tr><th scope=\"row\">1,000<\/th><td>6.53<\/td><td>32.53<\/td><\/tr><\/tbody><\/table><\/div><button type=\"button\" class=\"nx-in-btn\" data-nxi-csv=\"true\" hidden>Download the numbers (CSV)<\/button><h4 class=\"nx-h4\">Methods<\/h4><ol class=\"nx-in-meth\"><li>Gumbel extreme-value hazard <span class=\"nx-in-cite\">Gumbel (1958), Statistics of Extremes, Columbia University Press<\/span><\/li><li>Lognormal fragility (vulnerability) <span class=\"nx-in-cite\">FEMA (2003), HAZUS-MH Technical Manual<\/span><\/li><li>Exceedance-probability curve and average annual loss <span class=\"nx-in-cite\">Grossi and Kunreuther (2005), Catastrophe Modeling, Springer<\/span><\/li><li>Levee effect <span class=\"nx-in-cite\">Di Baldassarre and others (2013), Socio-hydrology, HESS 17<\/span><\/li><li>Uncertainty fan (parameter Monte Carlo) <span class=\"nx-in-cite\">Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333<\/span><\/li><li>One-at-a-time sensitivity (tornado) <span class=\"nx-in-cite\">Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley<\/span><\/li><\/ol><h4 class=\"nx-h4\">Data<\/h4><p class=\"nx-p\">The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page&#8217;s map can draw:<\/p><ul class=\"nx-in-src\"><li>Rivers <span class=\"nx-in-cite\">Natural Earth<\/span><\/li><li>Urban extents <span class=\"nx-in-cite\">Natural Earth<\/span><\/li><li>Cities <span class=\"nx-in-cite\">Natural Earth<\/span><\/li><li>Earthquakes M4.5+, past week (USGS) <span class=\"nx-in-cite\">U.S. Geological Survey \u00b7 register usgs.earthquake.summary-feeds<\/span><\/li><li>Rain rate, last half hour (IMERG) <span class=\"nx-in-cite\">NASA GPM IMERG Early \u00b7 register nasa.gibs.context-live-layers<\/span><\/li><li>River discharge forecast, 30 days with range (GloFAS via Open-Meteo) <span class=\"nx-in-cite\">Open-Meteo.com (CC BY 4.0), Copernicus GloFAS \u00b7 register open-meteo.public-pages<\/span><\/li><li>Built-up and settlement extent, 2010 (HBASE, Landsat) <span class=\"nx-in-cite\">CIESIN\/Columbia, NASA SEDAC HBASE \u00b7 register sedac.context-urban-layers<\/span><\/li><\/ul><\/div><\/details><figcaption class=\"nx-caption\">Loss exceedance, average annual loss and probable maximum loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Gumbel extreme-value hazard (Gumbel (1958), Statistics of Extremes, Columbia University Press); Lognormal fragility (vulnerability) (FEMA (2003), HAZUS-MH Technical Manual); Exceedance-probability curve and average annual loss (Grossi and Kunreuther (2005), Catastrophe Modeling, Springer).<\/figcaption><div class=\"nx-in-pls\"><p class=\"nx-eyebrow\">Across the six pillars, led by Nexus Consortium<\/p><ul class=\"nx-in-pl\" aria-label=\"Where this instrument connects across the pillars\"><li data-pillar=\"labs\" data-hue=\"social\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Experiments<\/span> <a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/\">Labs<\/a><\/li><li data-pillar=\"foundry\" data-hue=\"infrastructure\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Built in<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/foundry\/\">Insurance Nexus in Foundry<\/a><\/li><li data-pillar=\"universe\" data-hue=\"water\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Tested at<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/universe\/\">Insurance Nexus in Universe<\/a><\/li><li data-pillar=\"standards\" data-hue=\"biodiversity\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Codified in<\/span> <a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/\">Finance, Insurance and the Economy in Standards<\/a><\/li><li data-pillar=\"reports\" data-hue=\"energy\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Published in<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/reports\/\">Insurance Nexus in Reports<\/a><\/li><li data-pillar=\"academy\" data-hue=\"risk\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Taught in<\/span> <a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/finance-economics\/\">Faculty of Finance and Economics in Academy<\/a><\/li><\/ul><\/div><span class=\"nx-in-keep\" hidden><i data-c=\"c1\"><\/i><i data-c=\"c2\"><\/i><i data-c=\"c3\"><\/i><i data-c=\"c4\"><\/i><i data-c=\"c5\"><\/i><i data-c=\"c6\"><\/i><i data-c=\"l0\"><\/i><i data-c=\"l1\"><\/i><i data-c=\"l2\"><\/i><i data-c=\"l3\"><\/i><i data-c=\"l5\"><\/i><i data-c=\"h3\"><\/i><i data-c=\"neg\"><\/i><i data-c=\"on0\"><\/i><i data-c=\"on1\"><\/i><i data-l=\"0\"><\/i><i data-l=\"1\"><\/i><i data-l=\"2\"><\/i><i data-l=\"3\"><\/i><i data-l=\"4\"><\/i><i data-l=\"5\"><\/i><i data-on=\"0\"><\/i><i data-on=\"1\"><\/i><i data-pol=\"p\"><\/i><i data-pol=\"n\"><\/i><i data-k=\"R\"><\/i><i data-k=\"B\"><\/i><i data-k=\"dash\"><\/i><i data-dash=\"true\"><\/i><i data-view=\"result\"><\/i><i data-nxi-live=\"on\"><\/i><i data-nxi-live=\"off\"><\/i><i class=\"nx-in-svg nx-in-grid nx-in-ax nx-in-tk nx-in-at nx-in-ln nx-in-band nx-in-lbl nx-in-lk nx-in-ref nx-in-reft nx-in-mk nx-in-x nx-in-rect nx-in-val nx-in-rowl nx-in-cl nx-in-arrow nx-in-pol nx-in-cln nx-in-loop nx-in-edge nx-in-node nx-in-nl nx-in-reach nx-in-trib nx-in-agent nx-in-flow nx-in-legend nx-in-sw nx-in-note nx-in-loops nx-in-3d nx-in-cv nx-in-field nx-in-fl nx-in-fv nx-in-range nx-in-btn nx-in-err nx-in-tbl nx-in-cite\"><\/i><\/span><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-4\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by artificial intelligence and machine learning on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Aerial and satellite imagery<\/td><td data-label=\"Role\">Exposure attributes<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Labelled claims histories<\/td><td data-label=\"Role\">Training and testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Property inspection records<\/td><td data-label=\"Role\">Ground truth for imagery models<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Demographic reference data<\/td><td data-label=\"Role\">Fairness testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gra\/A01\/stem\/ai-ml\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A01\/stem\/ai-ml\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-2\" data-tone=\"alt\" data-hue=\"climate\" aria-label=\"Exposure, loss and capital, Validation, Connections\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Exposure, loss and capital, Validation, Connections\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-1\" aria-controls=\"nx-sec-audience\" aria-selected=\"true\" data-key=\"p1\">Exposure, loss and capital<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-2\" aria-controls=\"nx-sec-checks\" aria-selected=\"false\" data-key=\"p2\">Validation<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-3\" aria-controls=\"nx-sec-connections\" aria-selected=\"false\" data-key=\"p3\">Connections<\/button><\/div><section id=\"nx-sec-audience\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" aria-labelledby=\"nx-s-5\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Exposure, loss and capital<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">What machine learning in insurance means for exposure, loss and capital.<\/h2><p class=\"nx-lede\">The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"finance\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"exposure-loss\"><h3 class=\"nx-h4\">Exposure and loss at return periods<\/h3><p class=\"nx-p nx-au-what\">How losses from machine learning in insurance grow with severity, gross and net of reinsurance, against capital.<\/p><div class=\"nx-au-cols\"><div class=\"nx-au-fig\"><svg class=\"nx-au-svg\" viewBox=\"0 0 560 214\" role=\"img\" aria-label=\"Exceedance probability curve, gross and net loss by return period\" focusable=\"false\"><line class=\"nx-au-ax\" x1=\"50\" y1=\"186\" x2=\"540\" y2=\"186\"\/><line class=\"nx-au-ax\" x1=\"50\" y1=\"16\" x2=\"50\" y2=\"186\"\/><path class=\"nx-au-ln\" d=\"M50 156.3l163.3-40.4 163.4-27.6 163.3-46.8\"\/><path class=\"nx-au-ac\" d=\"M50 164.8l163.3-23.4 163.4-12.8 163.3-25.5\"\/><text class=\"nx-au-t\" x=\"50\" y=\"202\" text-anchor=\"middle\">1 in 10<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"50.0\" cy=\"164.8\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"213.33\" y=\"202\" text-anchor=\"middle\">1 in 50<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"213.3\" cy=\"141.4\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"376.67\" y=\"202\" text-anchor=\"middle\">1 in 100<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"376.7\" cy=\"128.6\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"540\" y=\"202\" text-anchor=\"middle\">1 in 250<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"540.0\" cy=\"103.1\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"534\" y=\"33.5\" text-anchor=\"end\">gross<\/text><text class=\"nx-au-t\" x=\"526\" y=\"129.13\" text-anchor=\"end\">net of reinsurance<\/text><text class=\"nx-au-t\" x=\"44\" y=\"20\" text-anchor=\"end\">80<\/text><text class=\"nx-au-t\" x=\"44\" y=\"190\" text-anchor=\"end\">0<\/text><\/svg><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Return period<\/th><th scope=\"col\">Annual probability<\/th><th scope=\"col\">Gross loss<\/th><th scope=\"col\">Net loss<\/th><th scope=\"col\">Net, capital = 100<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Return period\">1 in 10<\/td><td data-label=\"Annual probability\">0.10<\/td><td data-label=\"Gross loss\">14<\/td><td data-label=\"Net loss\">10<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">10<\/span><\/td><\/tr><tr><td data-label=\"Return period\">1 in 50<\/td><td data-label=\"Annual probability\">0.02<\/td><td data-label=\"Gross loss\">33<\/td><td data-label=\"Net loss\">21<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">21<\/span><\/td><\/tr><tr><td data-label=\"Return period\">1 in 100<\/td><td data-label=\"Annual probability\">0.01<\/td><td data-label=\"Gross loss\">46<\/td><td data-label=\"Net loss\">27<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">27<\/span><\/td><\/tr><tr><td data-label=\"Return period\">1 in 250<\/td><td data-label=\"Annual probability\">0.004<\/td><td data-label=\"Gross loss\">68<\/td><td data-label=\"Net loss\">39<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">39<\/span><\/td><\/tr><tr><td data-label=\"Return period\">Average annual loss<\/td><td data-label=\"Annual probability\"><\/td><td data-label=\"Gross loss\">3.1<\/td><td data-label=\"Net loss\">2.2<\/td><td data-label=\"Net, capital = 100\">2.2<\/td><\/tr><\/tbody><\/table><\/div><\/div><figcaption class=\"nx-caption\"><span>Illustrative loss index for a example portfolio (capital = 100): the exceedance-probability form insurers and supervisors read. Not data on any institution, not a price or a rating.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">NGFS climate scenarios \u00b7 Network for Greening the Financial System<\/span><span class=\"nx-chip\">IFRS S2 Climate-related Disclosures \u00b7 ISSB<\/span><span class=\"nx-chip\">TCFD recommendations \u00b7 Financial Stability Board TCFD<\/span><span class=\"nx-chip\">Principles for the effective management of climate-related financial risks \u00b7 Basel Committee on Banking Supervision<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section nx-band-panel\" data-hue=\"health\" data-variant=\"split\" aria-labelledby=\"nx-s-6\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Temporal and held-out validation<\/td><td data-label=\"How it is done\">Models are tested on later periods and unseen regions, not only on random splits<\/td><\/tr><tr><td data-label=\"Check\">Calibration and drift monitoring<\/td><td data-label=\"How it is done\">Predicted probabilities are compared with outcomes over time, and drift triggers recalibration<\/td><\/tr><tr><td data-label=\"Check\">Fairness and explainability review<\/td><td data-label=\"How it is done\">Outcome disparities and explanations are reviewed before deployment and periodically after<\/td><\/tr><tr><td data-label=\"Check\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 human-oversight\" data-tk=\"Nexus risk vocabulary\" data-td=\"Arrangements that keep competent people able to understand, monitor and override automated decisions.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Human oversight<\/a><\/td><td data-label=\"How it is done\">Decisions adverse to customers have a documented human review and an appeal route<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-7\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/underwriting-and-pricing-risk\/\"><span>Underwriting and pricing risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/property-and-casualty\/\"><span>Property and casualty<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/health-and-life-insurance\/\"><span>Health and life insurance<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/claims-and-operational-resilience\/\"><span>Claims and operational resilience<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/climate-and-nature-risk\/\"><span>Climate and nature risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/insurtech-and-ai\/\"><span>Insurtech and AI<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-3\" data-hue=\"climate\" aria-label=\"Stewardship, Connected, Questions\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Stewardship, Connected, Questions\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-1\" aria-controls=\"nx-sec-stewardship\" aria-selected=\"true\" data-key=\"p1\">Stewardship<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-2\" aria-controls=\"nx-s-9-sec\" aria-selected=\"false\" data-key=\"p2\">Connected<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-3\" aria-controls=\"nx-s-10-sec\" aria-selected=\"false\" data-key=\"p3\">Questions<\/button><\/div><section id=\"nx-sec-stewardship\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" aria-labelledby=\"nx-s-8\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/ai-autonomous-systems\/\">Artificial Intelligence and Autonomous Systems<\/a> stewards artificial intelligence and machine learning for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">Canonical page: Artificial intelligence and machine learning<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-9-sec\" class=\"nx-section nx-band-panel\" data-hue=\"health\" aria-labelledby=\"nx-s-9\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/ai-autonomous-systems\/stem\/ai-ml\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr><\/span><\/span><span class=\"nx-dest-title\">Machine learning behaviour<\/span><span class=\"nx-dest-why\">Artificial Intelligence and Autonomous Systems stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-10-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-10\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of artificial intelligence and machine learning in this field?<\/summary><div class=\"nx-answer\">Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\">Reports<\/a> is the Consortium&#8217;s quarterly publication. 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