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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n4xu4ggq.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n4xu4ggq.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n4xu4ggq.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n4xu4ggq.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n4xu4ggq.nxe :where(ul,ol){padding-left:20px}#nxe-n4xu4ggq.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n4xu4ggq.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n4xu4ggq.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n4xu4ggq.nxe svg{display:none}#nxe-n4xu4ggq.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n4xu4ggq.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n4xu4ggq.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-hero\" data-layout=\"console\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>A-P07 \u00b7 TOPIC \u00b7 credit-and-pricing-models<\/strong>GRA dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GRA<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">AI and Financial Model Risk<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 AI and Financial Model Risk<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Credit and pricing models<\/h1><p class=\"nx-tagline\">Credit scores, probability of default models and insurance pricing models decide who gets credit or cover and at what price. Machine learning can improve prediction and also introduce opacity, instability and proxy discrimination. These models also feed provisioning and capital. This page sets out how credit and pricing models fail, how their risks are measured, the data they depend on and who decides how they are used.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><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\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Dossier<\/strong><\/span><span class=\"nx-eyebrow\">A-P07 \u00b7 topic<\/span><\/div><ol class=\"nx-register-rows\"><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-register-n\">01<\/span><span class=\"nx-register-k\">In brief<\/span><span class=\"nx-register-v\">Definition and scope<\/span><\/a><\/li><li><a href=\"#nx-sec-question\"><span class=\"nx-register-n\">02<\/span><span class=\"nx-register-k\">The question<\/span><span class=\"nx-register-v\">4 mechanisms<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-register-n\">03<\/span><span class=\"nx-register-k\">Key terms<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-field\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Field<\/span><span class=\"nx-register-v\">H3 field plate<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">6<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Pipeline<\/span><span class=\"nx-register-v\">Run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">5<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">08<\/span><span class=\"nx-register-k\">Decision holders<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-methods\"><span class=\"nx-register-n\">09<\/span><span class=\"nx-register-k\">Methods<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GRA \u00b7 In development \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>Assets on the grid, and the\u2026<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Credit and pricing models: how the\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-2\"><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\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Credit scoring and pricing model risk is the risk that models deciding who gets credit or insurance, and at what price, are inaccurate, unstable or unfair. It arises from historical data, changing conditions and proxies, and feeds provisions and capital. Credit committees, validators, conduct supervisors and audit committees hold the decisions.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><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\">Spatial finance<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Assets on the grid, and the portfolio they make.<\/h2><p class=\"nx-lede\">Where a portfolio's assets sit, and how much of its value is most exposed. Hypothetical assets, not an assessment.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"asset-geocode\" data-gl=\"sf:asset-geocode\" data-sf=\"asset-geocode\" data-sx-credit=\"Natural Earth (public domain) | hypothetical assets and design scores\" data-sx-src=\"Natural Earth ~ public domain ~ https:\/\/www.naturalearthdata.com\/ | Design scenario values by the Global Risks Alliance ~ illustrative, not data\" data-sf-learn-views=\"quality\" aria-label=\"Interactive map: sixteen hypothetical assets at real towns, coloured by a design hazard score. The panel chooses a hazard or an asset.\" 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\"><figcaption class=\"nx-fk-cap\"><span>Sixteen assets at real towns, and the share of portfolio value in the most exposed places.<\/span><span data-honesty=\"true\">Hypothetical assets and design scores, not advice.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/credit-and-pricing-models\/#map\" data-nxf-title=\"AI and Financial Model Risk live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-4\"><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\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Credit and pricing models: how the risk develops.<\/h2><p class=\"nx-lede\">Finance-specific automated decisions. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"M14 2v4a2 2 0 0 0 2 2h4\"\/><path d=\"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3\"\/><path d=\"m9 18-1.5-1.5\"\/><circle cx=\"5\" cy=\"14\" r=\"3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Data reflect the past<\/h3><p class=\"nx-p nx-mt-1\">Models learn from historical lending and claims that reflect past decisions, economic conditions and exclusions. Applicants unlike past borrowers are scored with less reliable evidence.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Performance degrades in new conditions<\/h3><p class=\"nx-p nx-mt-1\">Models fitted in stable periods can misjudge default or claims when rates, inflation or unemployment shift, which is when accurate risk estimates matter most.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Proxies create unfair outcomes<\/h3><p class=\"nx-p nx-mt-1\">Variables correlated with protected characteristics can produce disparate outcomes without using those characteristics directly. Law and supervisors constrain which factors may be used.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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\"><circle cx=\"8\" cy=\"8\" r=\"6\"\/><path d=\"M18.09 10.37A6 6 0 1 1 10.34 18\"\/><path d=\"M7 6h1v4\"\/><path d=\"m16.71 13.88.7.71-2.82 2.82\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Errors reach capital and provisions<\/h3><p class=\"nx-p nx-mt-1\">Probability of default and loss given default estimates feed expected credit loss provisions and capital. Systematic optimism understates both until losses arrive.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><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\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Probability of default<\/td><td data-label=\"Definition\">The estimated likelihood that a borrower defaults within a set horizon, usually one year.<\/td><\/tr><tr><td data-label=\"Term\">Calibration<\/td><td data-label=\"Definition\">The agreement between predicted probabilities and the rates actually observed.<\/td><\/tr><tr><td data-label=\"Term\">Proxy discrimination<\/td><td data-label=\"Definition\">Unfair outcomes caused by variables that correlate with protected characteristics.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-6\"><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\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/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\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Discriminatory power of scorecards<\/td><td data-label=\"Unit\">Gini coefficient or area under the curve<\/td><td data-label=\"Cadence\">quarterly<\/td><td data-label=\"Source type\">Model monitoring<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Calibration of probability of default<\/td><td data-label=\"Unit\">observed default rate against predicted, ratio<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Back-testing<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Population stability index<\/td><td data-label=\"Unit\">dimensionless<\/td><td data-label=\"Cadence\">monthly<\/td><td data-label=\"Source type\">Model monitoring<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Approval rate differences across groups<\/td><td data-label=\"Unit\">ratio of approval rates<\/td><td data-label=\"Cadence\">periodic<\/td><td data-label=\"Source type\">Fair lending analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Overrides of model decisions<\/td><td data-label=\"Unit\">share of decisions<\/td><td data-label=\"Cadence\">monthly<\/td><td data-label=\"Source type\">Decision records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Expected credit loss model adjustments<\/td><td data-label=\"Unit\">currency of post-model adjustments<\/td><td data-label=\"Cadence\">quarterly<\/td><td data-label=\"Source type\">Financial statements<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/credit-and-pricing-models\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><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\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Credit and pricing models as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-a-p07-topics-credit-and-pricing-models-sr-t nx-dag-a-p07-topics-credit-and-pricing-models-sr-d\" data-nx-z=\"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\"><title id=\"nx-dag-a-p07-topics-credit-and-pricing-models-sr-t\">Credit and pricing models: model risk lifecycle<\/title><desc id=\"nx-dag-a-p07-topics-credit-and-pricing-models-sr-d\">Every model is inventoried and tiered; development is documented; independent validation tests conceptual soundness, outcomes and benchmarks; approval sets limits of use; monitoring and periodic review keep it current until it is retired. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><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\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/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\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">EBA guidelines on loan origination and monitoring<\/td><td data-label=\"What it gives\">Expectations for credit decision models including automated models<\/td><td data-label=\"Licence class\">Free, EU reuse terms<\/td><\/tr><tr><td data-label=\"Source\">CFPB circulars on adverse action and complex algorithms<\/td><td data-label=\"What it gives\">United States expectations for explaining credit denials<\/td><td data-label=\"Licence class\">Public domain<\/td><\/tr><tr><td data-label=\"Source\">HMDA data<\/td><td data-label=\"What it gives\">United States mortgage application and outcome data<\/td><td data-label=\"Licence class\">Public domain<\/td><\/tr><tr><td data-label=\"Source\">Bank Pillar 3 credit risk disclosures<\/td><td data-label=\"What it gives\">Probability of default back-testing and model outcomes<\/td><td data-label=\"Licence class\">Public records, terms per bank<\/td><\/tr><tr><td data-label=\"Source\">Actuarial and insurance supervisory publications on pricing models<\/td><td data-label=\"What it gives\">Practice and expectations for pricing models<\/td><td data-label=\"Licence class\">Free, terms per publisher<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A-P07\/topics\/credit-and-pricing-models\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-9\"><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\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">AI and Financial Model Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Credit risk and pricing committees<\/td><td data-label=\"The question\">Which models to use and with what overlays<\/td><td data-label=\"Evidence this page serves\">Performance, calibration and fairness evidence<\/td><\/tr><tr><td data-label=\"Who decides\">Model validators<\/td><td data-label=\"The question\">Whether models are fit for purpose<\/td><td data-label=\"Evidence this page serves\">Back-testing and benchmark comparison<\/td><\/tr><tr><td data-label=\"Who decides\">Conduct and fair lending supervisors<\/td><td data-label=\"The question\">Whether model outcomes are lawful and fair<\/td><td data-label=\"Evidence this page serves\">Outcome data and adverse action practice<\/td><\/tr><tr><td data-label=\"Who decides\">Audit committees<\/td><td data-label=\"The question\">Whether provisioning models and overlays are reasonable<\/td><td data-label=\"Evidence this page serves\">Model outputs and adjustment rationale<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to credit and pricing model performance measures<\/li><li>Method note on testing for proxy discrimination<\/li><li>Brief on post-model adjustments and what they reveal<\/li><li>Questions for credit committees on model limits<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-10\"><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\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">What experts ask.<\/h2><p class=\"nx-lede\">Credit and pricing questions combine accuracy, fairness and legality.<\/p><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Does the model predict default or loss accurately across all customer groups?<\/li><li>Do input variables act as proxies for characteristics that law protects?<\/li><li>Can the institution explain to a customer why credit was refused or a price set?<\/li><li>How does the model perform through the economic cycle, not only in benign conditions?<\/li><li>How do alternative data sources affect accuracy, inclusion and privacy?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-11\"><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\">Mechanisms<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Mechanisms and drivers.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Credit models estimate the likelihood that a borrower will repay; pricing models estimate expected losses and costs. Both learn from historical data, which reflects past lending practices, economic conditions and social inequalities. A model can be accurate on average while performing poorly for groups underrepresented in the data, or can reproduce historical discrimination through correlated variables.<\/p><p class=\"nx-p\">Credit models are especially exposed to economic regime change: relationships that held in an expansion may break in a downturn. Some jurisdictions classify AI used to assess the creditworthiness of individuals as high-risk, with specific obligations for providers and users.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-12\"><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\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">How it is studied, and where the methods stop.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Method or data<\/th><th scope=\"col\">What it shows<\/th><th scope=\"col\">Limits<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Method or data\">Discrimination and calibration testing<\/td><td data-label=\"What it shows\">Accuracy and calibration across groups<\/td><td data-label=\"Limits\">Protected attributes may not be recorded<\/td><\/tr><tr><td data-label=\"Method or data\">Fairness metrics<\/td><td data-label=\"What it shows\">Disparities in outcomes or errors<\/td><td data-label=\"Limits\">Different metrics can conflict<\/td><\/tr><tr><td data-label=\"Method or data\">Explanation methods<\/td><td data-label=\"What it shows\">Main drivers of individual decisions<\/td><td data-label=\"Limits\">Approximate explanations of complex models<\/td><\/tr><tr><td data-label=\"Method or data\">Through-the-cycle backtesting<\/td><td data-label=\"What it shows\">Performance across conditions<\/td><td data-label=\"Limits\">Few downturns in the data<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Calibration testing \u00b7 Drift statistics \u00b7 Group outcome comparison<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/ai-ml\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Artificial intelligence and machine learning<\/span><span class=\"nx-mledger-role\">Gradient-boosted credit models \u00b7 Language models in operations \u00b7 Anomaly detection<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\" aria-label=\"Artificial intelligence and machine learning: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Prediction intervals for loss models \u00b7 Model ensembles \u00b7 Model risk capital or buffers<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/stem\/ai-assurance\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">AI evaluation and assurance engineering<\/span><span class=\"nx-mledger-role\">Validation of machine learning models \u00b7 Red-teaming generative AI \u00b7 Fairness assessment<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/engineering\/ai-assurance\/\" aria-label=\"AI evaluation and assurance engineering: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Performance measures depend on the period and population tested, and few periods include severe stress. Fairness testing is limited where protected characteristics are not recorded and must be inferred. Pricing models are proprietary. Public outcome data exist for some markets only. AI and Financial Model Risk explains methods; it does not assess any firm's models, credit decisions or prices and gives no advice.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What does a Gini coefficient measure for a scorecard?<\/summary><div class=\"nx-answer\">How well the score ranks borrowers who later default above those who do not; it measures ranking power, not whether predicted default rates are accurate.<\/div><\/details><details><summary>What is a post-model adjustment?<\/summary><div class=\"nx-answer\">A management overlay applied to model output, often for expected credit loss, when the model is judged not to capture current conditions.<\/div><\/details><details><summary>Can a model discriminate without using protected characteristics?<\/summary><div class=\"nx-answer\">Yes. Variables that correlate with protected characteristics can produce disparate outcomes, which is why fairness testing looks at outcomes as well as inputs.<\/div><\/details><details><summary>Does alternative data improve inclusion?<\/summary><div class=\"nx-answer\">It can help people with thin credit files, but it also raises privacy, accuracy and fairness questions that need testing.<\/div><\/details><details><summary>Does the platform assess lenders' models?<\/summary><div class=\"nx-answer\">No. GRA does not assess, validate or approve any model.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around AI and Financial Model Risk.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-risk-governance\/\">Model risk governance <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/generative-ai-in-finance\/\">Generative AI in finance <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/explainability-and-fairness\/\">Explainability and fairness <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-monitoring-and-drift\/\">Model monitoring and drift <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/human-in-the-loop-controls\/\">Human in the loop controls <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/third-party-models\/\">Third party models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/\">AI and Financial Model Risk home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/\">The Global Risks Alliance home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/trust\/\">GRA governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">AI and Financial Model Risk<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@globalriskalliance.com?subject=AI%20and%20Financial%20Model%20Risk%3A%20Credit%20and%20pricing%20models\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><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\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/credit-and-pricing-models\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/credit-and-pricing-models\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What does a Gini coefficient measure for a scorecard?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"How well the score ranks borrowers who later default above those who do not; it measures ranking power, not whether predicted default rates are accurate.\"}},{\"@type\":\"Question\",\"name\":\"What is a post-model adjustment?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A management overlay applied to model output, often for expected credit loss, when the model is judged not to capture current conditions.\"}},{\"@type\":\"Question\",\"name\":\"Can a model discriminate without using protected characteristics?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. 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