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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n2hajrkr.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 AI and Financial Model Risk<\/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\">Probability and statistics for AI and financial model risk<\/h1><p class=\"nx-scene-lede\">Probability and statistics underpin financial models: estimating default and claim rates, testing calibration, measuring discrimination and detecting drift. On this platform they supply the measures and tests that show whether a model is performing as intended.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/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\/ai-financial-model-risk\/stem\/\">Back to STEM foundation of AI and Financial Model Risk<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"project\" 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\">Probability and statistics for AI and financial model risk 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\">Probability and statistics<\/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\/systems-modelling\/\">Systems Science and Modelling<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\">AI and Financial <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 model-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"Risk of harm from decisions based on models that are wrong, misused or applied outside their validity domain.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Model Risk<\/a><\/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\">3 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\">3 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\/mathematics\/probability-statistics\/\">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=\"finance\" aria-label=\"Application, Exposure, loss and capital, 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, Exposure, loss and capital, 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-audience\" aria-selected=\"false\" data-key=\"p2\">Exposure, loss and capital<\/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=\"finance\" 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 probability and statistics does here.<\/h2><p class=\"nx-lede\">Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In artificial intelligence and financial stability, statistical methods quantify the likelihood and uncertainty of losses 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>. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">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=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Calibration testing<\/h3><p class=\"nx-p nx-mt-1\">Predicted default or claim probabilities are compared with observed rates by band, using binomial and other tests suited to the sample size.<\/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=\"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Drift statistics<\/h3><p class=\"nx-p nx-mt-1\">Population stability and distribution distance measures are computed between periods to detect shifts in model inputs and scores.<\/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\">Group outcome comparison<\/h3><p class=\"nx-p nx-mt-1\">Outcome and error rates are compared across groups with confidence intervals, so differences are not read from noise.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section nx-band-panel\" data-hue=\"project\" 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\">Exposure, loss and capital<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">What it 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=\"transmission\"><h3 class=\"nx-h4\">Transmission channels<\/h3><p class=\"nx-p nx-au-what\">How a shock in Probability and statistics for AI and financial model risk reaches credit, market, underwriting, liquidity and operational risk, and feeds back.<\/p><div class=\"nx-au-fig\"><svg class=\"nx-au-svg\" viewBox=\"0 0 560 268\" role=\"img\" aria-label=\"Transmission channels from shocks through micro and macro channels to financial risks\" focusable=\"false\" data-nx-z=\"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\"><\/svg><\/div><figcaption class=\"nx-caption\"><span>Channel structure after the Basel Committee&#8217;s work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.<\/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-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 probability and statistics 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\/ai-financial-model-risk\/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\">Decision and outcome records<\/td><td data-label=\"Role\">Performance testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Score distributions<\/td><td data-label=\"Role\">Drift detection<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Reference portfolios<\/td><td data-label=\"Role\">Benchmarks<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/stem\/probability-statistics\/#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\/A-P07\/stem\/probability-statistics\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><\/div><\/div><\/section><section id=\"nx-s-5-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-5\"><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\">Process<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How the process runs.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-launch\" id=\"explore-nx-pe-catmodel\" data-kind=\"explorer\" data-js><div class=\"nx-launch-row\"><div class=\"nx-launch-text\"><p class=\"nx-launch-eb\">Process explorer \u00b7 Process<\/p><h3 class=\"nx-launch-t\" id=\"explore-nx-pe-catmodel-t\">Probability and statistics for AI and financial model risk: Catastrophe model pipeline<\/h3><p class=\"nx-launch-facts\"><span>10 steps<\/span><span class=\"nx-launch-sep\" aria-hidden=\"true\">\u00b7<\/span><span>5 stages<\/span><\/p><\/div><button type=\"button\" class=\"nx-launch-open\" aria-expanded=\"false\" aria-controls=\"explore-nx-pe-catmodel-body\" data-open-label=\"Open explorer\" data-close-label=\"Close explorer\"><svg class=\"nx-launch-i nx-launch-play\" viewBox=\"0 0 14 14\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M2.5 1v12L13 7z\"\/><\/svg><svg class=\"nx-launch-i nx-launch-x\" viewBox=\"0 0 14 14\" aria-hidden=\"true\" focusable=\"false\"><path d=\"m2.5 2.5 9 9m0-9-9 9\"\/><\/svg><span>Open explorer<\/span><\/button><\/div><div class=\"nx-launch-body\" id=\"explore-nx-pe-catmodel-body\" role=\"region\" aria-labelledby=\"explore-nx-pe-catmodel-t\" tabindex=\"-1\"><div class=\"nx-pe\" id=\"nx-pe-catmodel\" data-nx-pe=\"nx-pe-catmodel\" data-active=\"catmodel\"><div class=\"nx-pe-shell\"><p class=\"nx-pe-eb\">Process<\/p><h2 class=\"nx-pe-title\">Probability and statistics for AI and financial model risk: Catastrophe model pipeline<\/h2><p class=\"nx-pe-cap\">Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 average-annual-loss\" data-tk=\"Nexus risk vocabulary\" data-td=\"The long-run expected loss per year, averaged over all possible events; it equals the area under the loss exceedance curve.\" data-ts=\"NRS-C1\" data-tr=\"draft\">average annual loss<\/a>, exceedance curves, PML and TVaR.<\/p><ol class=\"nx-pe-steps\"><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">01 Hazard<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">01<\/span><span class=\"nx-pe-st\">Event catalogue<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Model developer<\/span><span class=\"nx-pe-sm\">The hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">03<\/span><span class=\"nx-pe-st\">Hazard module<\/span><span class=\"nx-pe-k\">Model \u00b7 Model developer<\/span><span class=\"nx-pe-sm\">For each event the hazard module gives the local <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 hazard-intensity\" data-tk=\"Nexus risk vocabulary\" data-td=\"The physical or functional strength of a hazard at a location, expressed through a measure appropriate to that hazard, such as depth, speed, shaking or concentration.\" data-ts=\"NRS-C1\" data-tr=\"draft\">hazard intensity<\/a> that the vulnerability module reads.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">02 Exposure and damage<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">02<\/span><span class=\"nx-pe-st\">Exposure<\/span><span class=\"nx-pe-k\">Dataset \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">The exposure module houses the portfolio data, such as location-specific information.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">04<\/span><span class=\"nx-pe-st\">Vulnerability module<\/span><span class=\"nx-pe-k\">Model \u00b7 Model developer<\/span><span class=\"nx-pe-sm\">The vulnerability module quantifies the expected damage to the exposures from an event.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">03 Financial<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">05<\/span><span class=\"nx-pe-st\">Financial module<\/span><span class=\"nx-pe-k\">Model \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">The financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">06<\/span><span class=\"nx-pe-st\">Event loss table<\/span><span class=\"nx-pe-k\">Dataset \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">An event loss table lists each event with its annual occurrence probability and its loss for the portfolio.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">07<\/span><span class=\"nx-pe-st\">Year loss table<\/span><span class=\"nx-pe-k\">Dataset \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">Some models also sample simulated years from event losses. This outline does not describe their format.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">04 Metrics<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">08<\/span><span class=\"nx-pe-st\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 exceedance-probability\" data-tk=\"Nexus risk vocabulary\" data-td=\"The probability that a quantity will reach or exceed a given level over a given period.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Exceedance probability<\/a> curves<\/span><span class=\"nx-pe-k\">Model \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">The occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">09<\/span><span class=\"nx-pe-st\">AAL, PML and TVaR<\/span><span class=\"nx-pe-k\">Model \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">Average annual loss sums event probability times loss. PML is one point on the occurrence curve.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">05 Use of results<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">10<\/span><span class=\"nx-pe-st\">Use of results<\/span><span class=\"nx-pe-k\">Decision \u00b7 MRM committee<\/span><span class=\"nx-pe-sm\">The tail metrics are used when considering a portfolio&#8217;s exposure and the level of catastrophe reinsurance an insurer requires.<\/span><\/li><\/ol><\/li><\/ol><p class=\"nx-pe-foot\"><span>Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-2\" data-hue=\"finance\" aria-label=\"Validation, 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, 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-connections\" aria-selected=\"false\" data-key=\"p2\">Connections<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-3\" aria-controls=\"nx-sec-stewardship\" aria-selected=\"false\" data-key=\"p3\">Stewardship<\/button><\/div><section id=\"nx-sec-checks\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-variant=\"inset\" 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\">Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.<\/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\">Sample size<\/td><td data-label=\"How it is done\">Tests state power and minimum sample sizes<\/td><\/tr><tr><td data-label=\"Check\">Multiple testing<\/td><td data-label=\"How it is done\">Many simultaneous tests are corrected for chance findings<\/td><\/tr><tr><td data-label=\"Check\">Out-of-sample<\/td><td data-label=\"How it is done\">Performance is reported on data not used for fitting<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section nx-band-panel\" data-hue=\"project\" data-variant=\"split\" 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\">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\/ai-financial-model-risk\/topics\/model-risk-governance\/\"><span>Model risk governance<\/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\/ai-financial-model-risk\/topics\/credit-and-pricing-models\/\"><span>Credit and pricing models<\/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\/ai-financial-model-risk\/topics\/generative-ai-in-finance\/\"><span>Generative AI in finance<\/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\/ai-financial-model-risk\/topics\/explainability-and-fairness\/\"><span>Explainability and fairness<\/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\/ai-financial-model-risk\/topics\/model-monitoring-and-drift\/\"><span>Model monitoring and drift<\/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\/ai-financial-model-risk\/topics\/human-in-the-loop-controls\/\"><span>Human in the loop controls<\/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\/ai-financial-model-risk\/topics\/third-party-models\/\"><span>Third party models<\/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\/ai-financial-model-risk\/topics\/stress-and-scenario-models\/\"><span>Stress and scenario models<\/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><section id=\"nx-sec-stewardship\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" 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\">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\/systems-modelling\/\">Systems Science and Modelling<\/a> stewards probability and statistics 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\/probability-statistics\/\">Canonical page: Probability and statistics<\/a><\/p><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-3\" data-tone=\"alt\" data-hue=\"project\" 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-9-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-10-sec\" aria-selected=\"false\" data-key=\"p2\">Questions<\/button><\/div><section id=\"nx-s-9-sec\" class=\"nx-section nx-band-panel\" data-hue=\"project\" aria-labelledby=\"nx-s-9\" 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\">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\/systems-modelling\/stem\/probability-statistics\/\"><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\">Statistics for modelling<\/span><span class=\"nx-dest-why\">Systems Science and Modelling 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-2\"><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 probability and statistics in this field?<\/summary><div class=\"nx-answer\">Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.<\/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\/probability-statistics\/\">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=\"project\" 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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