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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-ny3fm8pn.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 model-monitoring-and-drift<\/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\">Model monitoring and drift<\/h1><p class=\"nx-tagline\">A model validated at launch can fail later as customers, products, markets and data pipelines change. Monitoring tracks performance, input distributions and outcomes to detect drift and trigger review. Machine learning models that retrain frequently add their own risks. This page sets out how drift arises, how monitoring detects it, the measures used, and who decides when a model must change or stop.<\/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>From a satellite tile to an exposure\u2026<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Model monitoring and drift: 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\">Model monitoring and drift is the ongoing tracking of model performance and inputs after deployment to detect when changes in data or relationships make a model unreliable. Outcomes often arrive late, and frequent retraining adds risk. Model owners, model risk functions, supervisors and data owners 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\">From a satellite tile to an exposure update.<\/h2><p class=\"nx-lede\">How satellite radar turns a flood into an exposure update. Synthetic scene; no real event or place.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"geoai-eo\" data-gl=\"sf:geoai-eo\" data-sf=\"geoai-eo\" data-sx-credit=\"synthetic scene and schematic model\" data-sx-src=\"Synthetic scene ~ illustrative, not data\" data-sf-learn-views=\"pipeline pr\" aria-label=\"Interactive figure: the four steps of a flood mapping workflow on a synthetic radar scene, from the tile to the detected flood and an update of affected buildings. The panel steps through or plays the sequence.\" data-nx-z=\"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\"><figcaption class=\"nx-fk-cap\"><span>How satellite radar maps a flood and updates the count of affected buildings.<\/span><span data-honesty=\"true\">Synthetic scene and schematic model; no real event or place.<\/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\/model-monitoring-and-drift\/#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\">Model monitoring and drift: how the risk develops.<\/h2><p class=\"nx-lede\">Changes over time and review thresholds. 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=\"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\">Data drift<\/h3><p class=\"nx-p nx-mt-1\">The distribution of inputs shifts as new customer segments arrive, products change or upstream systems alter fields. The model meets data it was not trained on.<\/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=\"12\" cy=\"12\" r=\"3\"\/><circle cx=\"19\" cy=\"5\" r=\"2\"\/><circle cx=\"5\" cy=\"19\" r=\"2\"\/><path d=\"M10.4 21.9a10 10 0 0 0 9.941-15.416\"\/><path d=\"M13.5 2.1a10 10 0 0 0-9.841 15.416\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Concept drift<\/h3><p class=\"nx-p nx-mt-1\">The relationship between inputs and outcomes changes, for example when an economic shock alters who defaults. Accuracy falls even if inputs look stable.<\/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=\"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><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Outcomes arrive late<\/h3><p class=\"nx-p nx-mt-1\">Defaults and claims emerge months or years after decisions, so true performance is known late. Proxies and early indicators fill the gap with uncertainty.<\/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=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Retraining adds risk<\/h3><p class=\"nx-p nx-mt-1\">Frequent retraining can absorb drift, and it can also learn from feedback loops or corrupted data. Each retrained version needs controls proportionate to its use.<\/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\">Data drift<\/td><td data-label=\"Definition\">A change in the distribution of a model's inputs compared with the training data.<\/td><\/tr><tr><td data-label=\"Term\">Concept drift<\/td><td data-label=\"Definition\">A change in the relationship between inputs and the outcome a model predicts.<\/td><\/tr><tr><td data-label=\"Term\">Monitoring threshold<\/td><td data-label=\"Definition\">A pre-set level of change that triggers review of a model.<\/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\">Population stability index by input<\/td><td data-label=\"Unit\">dimensionless<\/td><td data-label=\"Cadence\">monthly<\/td><td data-label=\"Source type\">Monitoring<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Performance against validation baseline<\/td><td data-label=\"Unit\">change in discrimination or calibration<\/td><td data-label=\"Cadence\">monthly or quarterly<\/td><td data-label=\"Source type\">Monitoring<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Monitoring alerts breaching thresholds<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">monthly<\/td><td data-label=\"Source type\">Monitoring records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Models retrained in period<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">quarterly<\/td><td data-label=\"Source type\">Model records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Data pipeline incidents affecting models<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">quarterly<\/td><td data-label=\"Source type\">Incident records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Time from alert to remediation<\/td><td data-label=\"Unit\">days<\/td><td data-label=\"Cadence\">per alert<\/td><td data-label=\"Source type\">Governance records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/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\/model-monitoring-and-drift\/#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 Model monitoring and drift 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-model-monitoring-and-drift-s-t nx-dag-a-p07-topics-model-monitoring-and-drift-s-d\" data-nx-z=\"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\"><title id=\"nx-dag-a-p07-topics-model-monitoring-and-drift-s-t\">Model monitoring and drift: model risk lifecycle<\/title><desc id=\"nx-dag-a-p07-topics-model-monitoring-and-drift-s-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\" 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\">PRA SS1\/23 principles on model monitoring<\/td><td data-label=\"What it gives\">Expectations for ongoing performance monitoring<\/td><td data-label=\"Licence class\">Free, Bank of England terms<\/td><\/tr><tr><td data-label=\"Source\">SR 11-7 on ongoing monitoring and outcomes analysis<\/td><td data-label=\"What it gives\">United States expectations<\/td><td data-label=\"Licence class\">Public domain<\/td><\/tr><tr><td data-label=\"Source\">EBA guidelines on loan origination and monitoring<\/td><td data-label=\"What it gives\">Monitoring of automated credit models<\/td><td data-label=\"Licence class\">Free, EU reuse terms<\/td><\/tr><tr><td data-label=\"Source\">Academic research on dataset shift<\/td><td data-label=\"What it gives\">Methods for detecting and adapting to drift<\/td><td data-label=\"Licence class\">Free to read, terms per publisher<\/td><\/tr><tr><td data-label=\"Source\">Bank Pillar 3 model back-testing disclosures<\/td><td data-label=\"What it gives\">Back-testing results for capital models<\/td><td data-label=\"Licence class\">Public records, terms per bank<\/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\/model-monitoring-and-drift\/#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\">Model owners<\/td><td data-label=\"The question\">When to recalibrate, redevelop or retire a model<\/td><td data-label=\"Evidence this page serves\">Monitoring results against thresholds<\/td><\/tr><tr><td data-label=\"Who decides\">Model risk functions<\/td><td data-label=\"The question\">What monitoring standards apply by tier<\/td><td data-label=\"Evidence this page serves\">Policy and monitoring evidence<\/td><\/tr><tr><td data-label=\"Who decides\">Supervisors<\/td><td data-label=\"The question\">Whether monitoring is adequate for capital and conduct models<\/td><td data-label=\"Evidence this page serves\">Back-testing and review findings<\/td><\/tr><tr><td data-label=\"Who decides\">Data owners<\/td><td data-label=\"The question\">How upstream changes are controlled<\/td><td data-label=\"Evidence this page serves\">Pipeline incident records<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to data drift and concept drift<\/li><li>Method note on monitoring thresholds and alerts<\/li><li>Brief on controls for frequently retrained models<\/li><li>Questions for model owners on late-arriving outcomes<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" 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\">Monitoring questions ask how soon a problem will be seen.<\/p><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Which performance measures are monitored, and how often?<\/li><li>How quickly are outcomes, such as defaults or claims, observed, and what fills the gap meanwhile?<\/li><li>Has the input data distribution shifted, and does it matter?<\/li><li>What thresholds trigger review, recalibration or withdrawal, and who set them?<\/li><li>How are continuously retrained models governed?<\/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\">Data drift occurs when the distribution of inputs changes, for example a new customer segment or a change in a data source. Concept drift occurs when the relationship between inputs and outcomes changes, for example when an economic shock alters how income predicts repayment. Concept drift is more dangerous and harder to detect, because outcomes may take months or years to observe.<\/p><p class=\"nx-p\">Feedback loops add complexity: a model's decisions change who becomes a customer, which changes future data. Retraining can restore performance but can also absorb errors or bias, so retrained models need governance of their own.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"project\" 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\">Performance tracking against outcomes<\/td><td data-label=\"What it shows\">Accuracy and calibration over time<\/td><td data-label=\"Limits\">Outcomes arrive with delay<\/td><\/tr><tr><td data-label=\"Method or data\">Distribution shift statistics<\/td><td data-label=\"What it shows\">Changes in input data<\/td><td data-label=\"Limits\">Shift does not always mean performance loss<\/td><\/tr><tr><td data-label=\"Method or data\">Benchmark and challenger models<\/td><td data-label=\"What it shows\">Relative performance<\/td><td data-label=\"Limits\">Challengers share some data limitations<\/td><\/tr><tr><td data-label=\"Method or data\">Threshold and alerting frameworks<\/td><td data-label=\"What it shows\">When to escalate<\/td><td data-label=\"Limits\">Thresholds involve judgement<\/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\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/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-ml\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/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\/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\">Monitoring thresholds are set by firms and can be too loose or too sensitive. True outcomes arrive late, so early monitoring relies on proxies. Drift measures detect change, not whether it matters. Public data on monitoring practice are scarce. AI and Financial Model Risk explains monitoring methods; it does not monitor any model or advise on thresholds.<\/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 is the difference between data drift and concept drift?<\/summary><div class=\"nx-answer\">Data drift is a change in the distribution of inputs; concept drift is a change in how inputs relate to outcomes. Either can reduce model accuracy.<\/div><\/details><details><summary>What is a population stability index?<\/summary><div class=\"nx-answer\">A measure comparing the distribution of a variable or score between two periods, used to detect shifts in the population a model scores.<\/div><\/details><details><summary>Should models retrain automatically?<\/summary><div class=\"nx-answer\">Automatic retraining can keep models current but needs controls on data quality, performance checks and approval proportionate to the model's use.<\/div><\/details><details><summary>Why is concept drift hard to detect?<\/summary><div class=\"nx-answer\">Because it involves changes in the relationship between inputs and outcomes, and outcomes such as defaults may only be known long after decisions are made.<\/div><\/details><details><summary>Is automatic retraining a solution?<\/summary><div class=\"nx-answer\">It can help but needs governance: retrained models can absorb errors, bias or temporary conditions, and must still be tested and approved.<\/div><\/details><details><summary>Does the platform monitor firms' models?<\/summary><div class=\"nx-answer\">No. GRA does not monitor, 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 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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%20Model%20monitoring%20and%20drift\">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\/model-monitoring-and-drift\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/ai-financial-model-risk\/topics\/model-monitoring-and-drift\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is the difference between data drift and concept drift?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Data drift is a change in the distribution of inputs; concept drift is a change in how inputs relate to outcomes. 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