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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-np7qt2jv.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 Institutional Funds 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\">Actuarial mathematics for pension funds<\/h1><p class=\"nx-scene-lede\">Actuarial mathematics turns a promise to pay into a number a board can manage: the present value of benefits, the contributions needed to fund them and their sensitivity to rates, inflation and survival. For institutional funds it is the measure of the liability side, and every assumption it rests on is a judgement that must be stated.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/institutional-funds-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\/institutional-funds-nexus\/stem\/\">Back to STEM foundation of Institutional Funds 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\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Actuarial mathematics for pension funds in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What actuarial mathematics does here<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-1\" data-hue=\"social\" aria-label=\"Application, Monitor, Data, Loss model\"><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, Monitor, Data, Loss model\"><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-s-3-sec\" aria-selected=\"false\" data-key=\"p2\">Monitor<\/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><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-4\" aria-controls=\"nx-sec-instrument\" aria-selected=\"false\" data-key=\"p4\">Loss model<\/button><\/div><section id=\"nx-sec-application\" class=\"nx-section nx-band-panel\" data-hue=\"social\" 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 actuarial mathematics does here.<\/h2><p class=\"nx-lede\">Actuarial mathematics applies probability, statistics and financial theory to measure and price contingent risks, particularly in insurance and pensions. It determines reserves and premiums for uncertain future obligations. In capital markets and investment and society and human development, actuarial methods determine whether risks can be insured and at what cost. They help assess accumulation of correlated losses across portfolios and regions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/actuarial-math\/\">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=\"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\">Liability valuation on several bases<\/h3><p class=\"nx-p nx-mt-1\">Projected benefit cash flows are discounted on funding, accounting, guarantee and buyout bases, so the board sees how the same promises look under each purpose and which assumptions drive the differences.<\/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=\"M5 22h14\"\/><path d=\"M5 2h14\"\/><path d=\"M17 22v-4.172a2 2 0 0 0-.586-1.414L12 12l-4.414 4.414A2 2 0 0 0 7 17.828V22\"\/><path d=\"M7 2v4.172a2 2 0 0 0 .586 1.414L12 12l4.414-4.414A2 2 0 0 0 17 6.172V2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Mortality and longevity projection<\/h3><p class=\"nx-p nx-mt-1\">Base tables are adjusted to scheme experience and combined with improvement models, and the liability is revalued under alternative improvement paths to show longevity sensitivity.<\/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=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Funding plan and recovery path<\/h3><p class=\"nx-p nx-mt-1\">Contributions, expected returns and benefit outgo are projected forward to show how a deficit is closed over the recovery period, and how the path changes under stressed returns or a weaker sponsor.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" 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\">Monitor<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Tail fit.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-funds-method-tail\" data-cx=\"funds.method-tail\" data-variant=\"section\" aria-label=\"Tail fit: illustrative console with example names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Tail fit<\/h3><p class=\"nx-fk-sub\">Institutional funds \u00b7 Actuarial mathematics for pension \u00b7 Run R-7350<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure&#8217;s values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Shape parameter uncertain<\/span><span class=\"nx-cx-prod\">GRA \u00b7 Institutional Funds Nexus<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Return level curve<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Return level curve\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-funds-method-tail-c0\" data-fk-xs=\"log,2,500,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-xf=\"rp\" data-fk-series=\"OEP|ink|l||2:3.5;5:9.3;10:16.4;20:25;50:35;100:46.5;200:59.3;500:73.6~AEP|acc|l||2:3.7;5:9.9;10:17.6;20:27.2;50:38.4;100:51.6;200:66.5;500:83.5\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"kv\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Method card<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Method<\/dt><dd>Actuarial mathematics<\/dd><\/div><div><dt>Approach<\/dt><dd>peaks over threshold<\/dd><\/div><div><dt>Threshold<\/dt><dd>98th percentile<\/dd><\/div><div><dt>Label<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>MODELLED<\/span><\/dd><\/div><\/dl><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Parameters<\/h4><p class=\"nx-fk-panel-sub\">generalised Pareto fit<\/p><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-funds-method-tail-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-funds-method-tail-t2\" aria-label=\"Parameters\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Parameter<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Estimate<\/button><\/th><th scope=\"col\" data-col=\"c2\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Lower<\/button><\/th><th scope=\"col\" data-col=\"c3\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Upper<\/button><\/th><th scope=\"col\" data-col=\"c4\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c4\">Note<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-funds-method-tail:r2-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Shape \u03be<\/td><td data-num=\"true\" data-sv=\"0.29\">0.29<\/td><td data-num=\"true\" data-sv=\"-0.04\">\u22120.04<\/td><td data-num=\"true\" data-sv=\"0.47\">0.47<\/td><td>heavy tail if above zero<\/td><\/tr><tr data-fk-key=\"cx-funds-method-tail:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Scale \u03c3<\/td><td data-num=\"true\" data-sv=\"3\">3.0<\/td><td data-num=\"true\" data-sv=\"1.1\">1.1<\/td><td data-num=\"true\" data-sv=\"5.1\">5.1<\/td><td>units of the variable<\/td><\/tr><tr data-fk-key=\"cx-funds-method-tail:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Exceedances<\/td><td data-num=\"true\" data-sv=\"68\">68<\/td><td data-num=\"true\"><\/td><td data-num=\"true\"><\/td><td>declustered<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Tail fit: the screen&#8217;s structure, terms and states for Institutional funds.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section nx-band-panel\" data-hue=\"social\" data-variant=\"split\" 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 actuarial mathematics 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\/institutional-funds-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\">Scheme membership and benefit data<\/td><td data-label=\"Role\">Projected benefit cash flows<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">National and actuarial mortality tables<\/td><td data-label=\"Role\">Base mortality and improvement<\/td><td data-label=\"Label\"><span class=\"nx-chip\">Published statistics<\/span><\/td><\/tr><tr><td data-label=\"Data\">Government bond and swap curves<\/td><td data-label=\"Role\">Discount rates and inflation expectations<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Scheme mortality experience studies<\/td><td data-label=\"Role\">Adjustment of tables to the membership<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Disclosed actuarial assumptions<\/td><td data-label=\"Role\">Comparison across funds<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gra\/A08\/stem\/actuarial-math\/#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\/A08\/stem\/actuarial-math\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-instrument\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-ns-layout=\"es-wide\" aria-labelledby=\"nx-s-5\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-4\"><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\">Loss model<\/span><\/div><h2 id=\"nx-s-5\" 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-tutional-funds-nexus-stem-actuarial-math-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-tutional-funds-nexus-stem-actuarial-math-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\/institutional-funds-nexus\/foundry\/\">Institutional Funds 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\/institutional-funds-nexus\/universe\/\">Institutional Funds 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\/institutional-funds-nexus\/reports\/\">Institutional Funds 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><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-2\" data-tone=\"alt\" data-hue=\"social\" aria-label=\"Validation, Exposure, loss and capital, Connections, Stewardship\"><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=\"Validation, Exposure, loss and capital, Connections, Stewardship\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-1\" aria-controls=\"nx-sec-checks\" aria-selected=\"true\" data-key=\"p1\">Validation<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-2\" aria-controls=\"nx-sec-audience\" aria-selected=\"false\" data-key=\"p2\">Exposure, loss and capital<\/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><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-4\" aria-controls=\"nx-sec-stewardship\" aria-selected=\"false\" data-key=\"p4\">Stewardship<\/button><\/div><section id=\"nx-sec-checks\" class=\"nx-section nx-band-panel\" data-hue=\"social\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\" 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\">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\">Assumptions are tested through experience studies and actual-versus-expected analysis. Independent actuarial review checks methods and results.<\/p><\/div><div class=\"nx-section-body\"><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\">Actual versus expected analysis<\/td><td data-label=\"How it is done\">Deaths, retirements and withdrawals are compared with what the assumptions implied, and persistent gaps prompt a change of assumption<\/td><\/tr><tr><td data-label=\"Check\">Analysis of surplus<\/td><td data-label=\"How it is done\">The change in funding between valuations is decomposed into returns, rates, experience and assumption changes, and the residual is reported<\/td><\/tr><tr><td data-label=\"Check\">Sensitivity reporting<\/td><td data-label=\"How it is done\">Liabilities are restated under shifts in <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 discount-rate\" data-tk=\"Nexus risk vocabulary\" data-td=\"The rate used to convert future costs and benefits into present values; its choice strongly affects the weight given to long-term risks.\" data-ts=\"NRS-C1\" data-tr=\"draft\">discount rate<\/a>, inflation and longevity so each assumption&#8217;s weight is visible<\/td><\/tr><tr><td data-label=\"Check\">Independent actuarial review<\/td><td data-label=\"How it is done\">Methods and assumptions are reviewed by an actuary not involved in the valuation, under professional standards<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Historical experience may not reflect emerging or correlated risks. Long-tail liabilities are sensitive to assumptions about future trends.<\/p><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-7\" 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\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Exposure, loss and capital<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">What actuarial mathematics for pension funds 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 actuarial mathematics for pension funds 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-connections\" class=\"nx-section nx-band-panel\" data-hue=\"social\" aria-labelledby=\"nx-s-8\" 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\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-8\" 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\/institutional-funds-nexus\/topics\/pension-system-resilience\/\"><span>Pension system 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\/institutional-funds-nexus\/topics\/long-term-risk\/\"><span>Long-term 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\/institutional-funds-nexus\/topics\/asset-liability-management\/\"><span>Asset-liability management<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" 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class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-9\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-4\"><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\">Stewardship<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/\">Actuarial and Quantitative Risk<\/a> stewards actuarial mathematics 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\/mathematics\/actuarial-math\/\">Canonical page: Actuarial mathematics<\/a><\/p><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-3\" data-hue=\"social\" aria-label=\"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=\"Connected, Questions\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-1\" aria-controls=\"nx-s-10-sec\" aria-selected=\"true\" data-key=\"p1\">Connected<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-2\" aria-controls=\"nx-s-11-sec\" aria-selected=\"false\" data-key=\"p2\">Questions<\/button><\/div><section id=\"nx-s-10-sec\" class=\"nx-section nx-band-panel\" data-hue=\"social\" aria-labelledby=\"nx-s-10\" 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\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-10\" 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:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/actuarial-math\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GRA<\/span><\/span><span class=\"nx-dest-title\">Actuarial mathematics at Actuarial and Quantitative Risk<\/span><span class=\"nx-dest-why\">Actuarial and Quantitative Risk 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-11-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-11\" 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\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-11\" 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 actuarial mathematics in this field?<\/summary><div class=\"nx-answer\">Historical experience may not reflect emerging or correlated risks. Long-tail liabilities are sensitive to assumptions about future trends.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Assumptions are tested through experience studies and actual-versus-expected analysis. Independent actuarial review checks methods and results.<\/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\/mathematics\/actuarial-math\/\">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=\"social\" 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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