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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 Banking Nexus<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><p class=\"nx-scene-accent nx-es-sig\"><strong>Spatial finance<\/strong><span>Where capital meets hazard, exposure and transition<\/span><\/p><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Machine learning in banking<\/h1><p class=\"nx-scene-lede\">Artificial intelligence and machine learning now support credit underwriting, fraud and financial crime detection, <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 early-warning\" data-tk=\"Nexus risk vocabulary\" data-td=\"Timely, actionable information about an impending hazard that lets people and institutions act to reduce harm before it occurs.\" data-ts=\"NRS-C1\" data-tr=\"draft\">early warning<\/a> of borrower stress and supervisory analysis. In banking they are both a tool and a source of risk: their outputs must be explainable, fair, robust and validated before they influence consequential decisions about customers or capital.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/stem\/\">Back to STEM foundation of Banking Nexus<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Machine learning in banking in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What artificial intelligence and machine learning does here<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-1\" data-hue=\"climate\" aria-label=\"Application, Monitor, Data, Stress test\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Application, Monitor, Data, Stress test\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-1\" aria-controls=\"nx-sec-application\" aria-selected=\"true\" data-key=\"p1\">Application<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-2\" aria-controls=\"nx-s-3-sec\" aria-selected=\"false\" data-key=\"p2\">Monitor<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-3\" aria-controls=\"nx-sec-data\" aria-selected=\"false\" data-key=\"p3\">Data<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-4\" aria-controls=\"nx-sec-instrument\" aria-selected=\"false\" data-key=\"p4\">Stress test<\/button><\/div><section id=\"nx-sec-application\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" data-variant=\"band\" aria-labelledby=\"nx-s-2\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">What artificial intelligence and machine learning does here.<\/h2><p class=\"nx-lede\">Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. They are increasingly used for hazard detection, forecasting and decision support, and are themselves a source of risk. In financial stability, capital markets and investment and climate and hazards, machine learning can scale detection and forecasting across interconnected <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a>. Its own failure modes must be weighed when automated outputs inform consequential decisions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z\"\/><path d=\"M9 13a4.5 4.5 0 0 0 3-4\"\/><path d=\"M6.003 5.125A3 3 0 0 0 6.401 6.5\"\/><path d=\"M3.477 10.896a4 4 0 0 1 .585-.396\"\/><path d=\"M6 18a4 4 0 0 1-1.967-.516\"\/><path d=\"M12 13h4\"\/><path d=\"M12 18h6a2 2 0 0 1 2 2v1\"\/><path d=\"M12 8h8\"\/><path d=\"M16 8V5a2 2 0 0 1 2-2\"\/><circle cx=\"16\" cy=\"13\" r=\".5\"\/><circle cx=\"18\" cy=\"3\" r=\".5\"\/><circle cx=\"20\" cy=\"21\" r=\".5\"\/><circle cx=\"20\" cy=\"8\" r=\".5\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Credit decisioning with explainability<\/h3><p class=\"nx-p nx-mt-1\">Gradient boosting and similar models are developed alongside interpretable scorecards; explanations of individual decisions are produced and tested for stability before any use in lending.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m8 11 2 2 4-4\"\/><circle cx=\"11\" cy=\"11\" r=\"8\"\/><path d=\"m21 21-4.3-4.3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Early warning and transaction monitoring<\/h3><p class=\"nx-p nx-mt-1\">Transaction and account data are used to flag borrowers showing early signs of distress and patterns associated with fraud, with every alert reviewed by a person.<\/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=\"M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z\"\/><path d=\"M14 2v4a2 2 0 0 0 2 2h4\"\/><path d=\"M10 9H8\"\/><path d=\"M16 13H8\"\/><path d=\"M16 17H8\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Text analysis for risk and supervision<\/h3><p class=\"nx-p nx-mt-1\">Language models and text classifiers extract risk signals from filings, incident reports and supervisory documents, with outputs checked against human review.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-3\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Monitor<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Model training.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-banking-method-ml\" data-cx=\"banking.method-ml\" data-variant=\"section\" aria-label=\"Model training: illustrative console with example names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Model training<\/h3><p class=\"nx-fk-sub\">Banking \u00b7 Machine learning in banking \u00b7 Run R-5640<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure&#8217;s values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Drift on one slice<\/span><span class=\"nx-cx-prod\">GRA \u00b7 Banking Nexus<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Evaluation by slice<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-banking-method-ml-t0\"><table class=\"nx-fk-table\" data-fk-table=\"cx-banking-method-ml-t0\" aria-label=\"Evaluation by slice\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Slice<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">AUC<\/button><\/th><th scope=\"col\" data-col=\"c2\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Calibration error<\/button><\/th><th scope=\"col\" data-col=\"c3\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Samples<\/button><\/th><th scope=\"col\" data-col=\"c4\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c4\">State<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-banking-method-ml:r0-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>All<\/td><td data-num=\"true\" data-sv=\"0.89\">0.89<\/td><td data-num=\"true\" data-sv=\"0.05\">0.05<\/td><td data-num=\"true\" data-sv=\"5695\">5695<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Pass<\/span><\/td><\/tr><tr data-fk-key=\"cx-banking-method-ml:r0-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Region A<\/td><td data-num=\"true\" data-sv=\"0.85\">0.85<\/td><td data-num=\"true\" data-sv=\"0.06\">0.06<\/td><td data-num=\"true\" data-sv=\"1734\">1734<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Review<\/span><\/td><\/tr><tr data-fk-key=\"cx-banking-method-ml:r0-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\" aria-current=\"true\" data-cur=\"true\"><td>Region B<\/td><td data-num=\"true\" data-sv=\"0.68\">0.68<\/td><td data-num=\"true\" data-sv=\"0.09\">0.09<\/td><td data-num=\"true\" data-sv=\"636\">636<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Review<\/span><\/td><\/tr><tr data-fk-key=\"cx-banking-method-ml:r0-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Rare class<\/td><td data-num=\"true\" data-sv=\"0.71\">0.71<\/td><td data-num=\"true\" data-sv=\"0.11\">0.11<\/td><td data-num=\"true\" data-sv=\"144\">144<\/td><td><span class=\"nx-fk-pill\" data-state=\"not\"><i aria-hidden=\"true\"><\/i>Below bar<\/span><\/td><\/tr><\/tbody><\/table><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"kv\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Method card<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Method<\/dt><dd>Artificial intelligence and machine learning<\/dd><\/div><div><dt>Split<\/dt><dd>time-based, no leakage<\/dd><\/div><div><dt>Features<\/dt><dd>56<\/dd><\/div><div><dt>Label<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>MODELLED<\/span><\/dd><\/div><\/dl><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Population stability<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Population stability\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-banking-method-ml-c2\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Drift index|ink|l||0:47.9;1:53.6;2:50.9;3:47.8;4:47.5;5:52.6;6:55.7;7:61.6;8:58.6;9:57.5;10:55.2;11:53.6;12:59.1;13:61.5;14:65.8;15:68.3;16:74.7;17:78.5;18:77.4;19:79.2;20:81.2;21:80.8;22:79.1;23:76\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Model training: the screen&#8217;s structure, terms and states for Banking.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" aria-labelledby=\"nx-s-4\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by artificial intelligence and machine learning on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Credit application and performance data<\/td><td data-label=\"Role\">Training and validation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Transaction records<\/td><td data-label=\"Role\">Monitoring and <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 early-warning\" data-tk=\"Nexus risk vocabulary\" data-td=\"Timely, actionable information about an impending hazard that lets people and institutions act to reduce harm before it occurs.\" data-ts=\"NRS-C1\" data-tr=\"draft\">early warning<\/a> features<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Public filings and supervisory publications<\/td><td data-label=\"Role\">Text corpora<\/td><td data-label=\"Label\"><span class=\"nx-chip\">Published statistics<\/span><\/td><\/tr><tr><td data-label=\"Data\">Cash-flow and alternative data used under consent and law<\/td><td data-label=\"Role\">Additional borrower features<\/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\/A02\/stem\/ai-ml\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A02\/stem\/ai-ml\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-instrument\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-ns-layout=\"es-wide\" aria-labelledby=\"nx-s-5\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stress test<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How a shock to assets becomes a system loss.<\/h2><p class=\"nx-lede\">The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 stress-test\" data-tk=\"Nexus risk vocabulary\" data-td=\"An assessment of how a system, portfolio or institution would perform under severe but plausible adverse scenarios, to find weaknesses before they are revealed by events.\" data-ts=\"NRS-C1\" data-tr=\"draft\">stress test<\/a>. Teaching model with stated illustrative values, not a prediction.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-in nx-fk\" id=\"nx-in-banking-nexus-stem-ai-ml-0\" data-nxi=\"debtrank\" data-nxi-id=\"debtrank\" data-nxi-z=\"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\" aria-label=\"How a shock to assets becomes a system loss\"><div class=\"nx-in-body\"><div class=\"nx-in-main\"><div class=\"nx-in-tabs\" data-nxi-tabs=\"true\" aria-label=\"Views of the model\" hidden><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"result\">Result<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"net\">Network<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"fan\">Uncertainty<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"tornado\">Sensitivity<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"loops\">Loops<\/button><\/div><div class=\"nx-in-stage\" id=\"nx-in-banking-nexus-stem-ai-ml-0-stage\" data-nxi-stage=\"true\" data-view=\"result\"><svg class=\"nx-in-svg\" viewBox=\"0 0 720 340\" role=\"img\" aria-label=\"Equity lost\" data-nx-z=\"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\"><\/svg><\/div><p class=\"nx-in-tip\" data-nxi-tip=\"true\" aria-live=\"polite\"><\/p><div class=\"nx-in-bar\" data-nxi-bar=\"true\" role=\"group\" aria-label=\"Play over time\" hidden><button type=\"button\" class=\"nx-in-btn\" data-nxi-prev=\"true\" aria-label=\"Step back\">&#8249;<\/button><button type=\"button\" class=\"nx-in-btn\" data-nxi-play=\"true\" aria-pressed=\"false\">Play<\/button><button type=\"button\" class=\"nx-in-btn\" data-nxi-next=\"true\" aria-label=\"Step forward\">&#8250;<\/button><span class=\"nx-in-step\" data-nxi-step=\"true\" aria-live=\"polite\"><\/span><\/div><\/div><div class=\"nx-in-side\"><p class=\"nx-eyebrow\">Controls<\/p><div class=\"nx-in-ctl\" data-nxi-ctl=\"true\"><dl class=\"nx-in-pv\"><div><dt>Fall in external assets<\/dt><dd>2.0%<\/dd><\/div><div><dt>Leverage (assets over equity)<\/dt><dd>10 times<\/dd><\/div><div><dt>Interbank share of assets<\/dt><dd>10%<\/dd><\/div><div><dt>Recovery on interbank claims<\/dt><dd>40%<\/dd><\/div><div><dt>Banks in the system<\/dt><dd>20<\/dd><\/div><div><dt>Core banks<\/dt><dd>20% of banks<\/dd><\/div><\/dl><\/div><p class=\"nx-eyebrow\">Readouts<\/p><dl class=\"nx-in-out\" data-nxi-out=\"true\" aria-live=\"polite\"><div><dt>Equity lost to the initial shock<\/dt><dd>18.0%<\/dd><\/div><div><dt>Equity lost after contagion<\/dt><dd>45.0%<\/dd><\/div><div><dt>Added by contagion (DebtRank)<\/dt><dd>27.0 points<\/dd><\/div><div><dt>Banks in default<\/dt><dd>0<\/dd><\/div><\/dl><\/div><\/div><details class=\"nx-in-more\"><summary>Numbers, methods and sources<\/summary><div class=\"nx-in-more-in\"><div class=\"nx-in-tblw\" data-nxi-table=\"true\"><table class=\"nx-in-tbl\"><thead><tr><th scope=\"col\">Round<\/th><th scope=\"col\">System equity lost (%)<\/th><th scope=\"col\">Banks in default<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>18.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>28.80<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">2<\/th><td>35.28<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">3<\/th><td>39.17<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">4<\/th><td>41.50<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">5<\/th><td>42.90<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">6<\/th><td>43.74<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">7<\/th><td>44.24<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">8<\/th><td>44.55<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">9<\/th><td>44.73<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">10<\/th><td>44.84<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">11<\/th><td>44.90<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">12<\/th><td>44.94<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">13<\/th><td>44.96<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">14<\/th><td>44.98<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">15<\/th><td>44.99<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">16<\/th><td>44.99<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">17<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">18<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">19<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">20<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">21<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">22<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">23<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">24<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">25<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">26<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">27<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">28<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">29<\/th><td>45.00<\/td><td>0<\/td><\/tr><tr><th scope=\"row\">30<\/th><td>45.00<\/td><td>0<\/td><\/tr><\/tbody><\/table><\/div><button type=\"button\" class=\"nx-in-btn\" data-nxi-csv=\"true\" hidden>Download the numbers (CSV)<\/button><h4 class=\"nx-h4\">Methods<\/h4><ol class=\"nx-in-meth\"><li>DebtRank <span class=\"nx-in-cite\">Battiston and others (2012), Scientific Reports 2:541<\/span><\/li><li>Differential DebtRank <span class=\"nx-in-cite\">Bardoscia and others (2015), PLOS ONE 10<\/span><\/li><li>Contagion in financial networks <span class=\"nx-in-cite\">Gai and Kapadia (2010), Proc. R. Soc. A 466<\/span><\/li><li>Clearing in financial networks <span class=\"nx-in-cite\">Eisenberg and Noe (2001), Management Science 47<\/span><\/li><li>Uncertainty fan (parameter Monte Carlo) <span class=\"nx-in-cite\">Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333<\/span><\/li><li>One-at-a-time sensitivity (tornado) <span class=\"nx-in-cite\">Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley<\/span><\/li><\/ol><h4 class=\"nx-h4\">Data<\/h4><p class=\"nx-p\">No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme&#8217;s own evidence.<\/p><\/div><\/details><figcaption class=\"nx-caption\">How a shock to assets becomes a system loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: DebtRank (Battiston and others (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia and others (2015), PLOS ONE 10); Contagion in financial networks (Gai and Kapadia (2010), Proc. R. Soc. A 466).<\/figcaption><div class=\"nx-in-pls\"><p class=\"nx-eyebrow\">Across the six pillars, led by Nexus Consortium<\/p><ul class=\"nx-in-pl\" aria-label=\"Where this instrument connects across the pillars\"><li data-pillar=\"labs\" data-hue=\"social\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Experiments<\/span> <a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/\">Labs<\/a><\/li><li data-pillar=\"foundry\" data-hue=\"infrastructure\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Built in<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/foundry\/\">Banking Nexus in Foundry<\/a><\/li><li data-pillar=\"universe\" data-hue=\"water\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Tested at<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/universe\/\">Banking Nexus in Universe<\/a><\/li><li data-pillar=\"standards\" data-hue=\"biodiversity\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Codified in<\/span> <a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/\">Finance, Insurance and the Economy in Standards<\/a><\/li><li data-pillar=\"reports\" data-hue=\"energy\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Published in<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/reports\/\">Banking Nexus in Reports<\/a><\/li><li data-pillar=\"academy\" data-hue=\"risk\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Taught in<\/span> <a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/finance-economics\/\">Faculty of Finance and Economics in Academy<\/a><\/li><\/ul><\/div><span class=\"nx-in-keep\" hidden><i data-c=\"c1\"><\/i><i data-c=\"c2\"><\/i><i data-c=\"c3\"><\/i><i data-c=\"c4\"><\/i><i data-c=\"c5\"><\/i><i data-c=\"c6\"><\/i><i data-c=\"l0\"><\/i><i data-c=\"l1\"><\/i><i data-c=\"l2\"><\/i><i data-c=\"l3\"><\/i><i data-c=\"l5\"><\/i><i data-c=\"h3\"><\/i><i data-c=\"neg\"><\/i><i data-c=\"on0\"><\/i><i data-c=\"on1\"><\/i><i data-l=\"0\"><\/i><i data-l=\"1\"><\/i><i data-l=\"2\"><\/i><i data-l=\"3\"><\/i><i data-l=\"4\"><\/i><i data-l=\"5\"><\/i><i data-on=\"0\"><\/i><i data-on=\"1\"><\/i><i data-pol=\"p\"><\/i><i data-pol=\"n\"><\/i><i data-k=\"R\"><\/i><i data-k=\"B\"><\/i><i data-k=\"dash\"><\/i><i data-dash=\"true\"><\/i><i data-view=\"result\"><\/i><i data-nxi-live=\"on\"><\/i><i data-nxi-live=\"off\"><\/i><i class=\"nx-in-svg nx-in-grid nx-in-ax nx-in-tk nx-in-at nx-in-ln nx-in-band nx-in-lbl nx-in-lk nx-in-ref nx-in-reft nx-in-mk nx-in-x nx-in-rect nx-in-val nx-in-rowl nx-in-cl nx-in-arrow nx-in-pol nx-in-cln nx-in-loop nx-in-edge nx-in-node nx-in-nl nx-in-reach nx-in-trib nx-in-agent nx-in-flow nx-in-legend nx-in-sw nx-in-note nx-in-loops nx-in-3d nx-in-cv nx-in-field nx-in-fl nx-in-fv nx-in-range nx-in-btn nx-in-err nx-in-tbl nx-in-cite\"><\/i><\/span><\/figure><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-2\" data-tone=\"alt\" data-hue=\"climate\" aria-label=\"Validation, Exposure, loss and capital, Connections, Stewardship\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Validation, Exposure, loss and capital, Connections, Stewardship\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-1\" aria-controls=\"nx-sec-checks\" aria-selected=\"true\" data-key=\"p1\">Validation<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-2\" aria-controls=\"nx-sec-audience\" aria-selected=\"false\" data-key=\"p2\">Exposure, loss and capital<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-3\" aria-controls=\"nx-sec-connections\" aria-selected=\"false\" data-key=\"p3\">Connections<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-4\" aria-controls=\"nx-sec-stewardship\" aria-selected=\"false\" data-key=\"p4\">Stewardship<\/button><\/div><section id=\"nx-sec-checks\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Temporal hold-out<\/td><td data-label=\"How it is done\">Models are evaluated on periods later than the training data, never on random splits alone<\/td><\/tr><tr><td data-label=\"Check\">Fairness testing<\/td><td data-label=\"How it is done\">Outcomes are compared across groups using several fairness definitions, and the trade-offs are reported<\/td><\/tr><tr><td data-label=\"Check\">Explanation stability<\/td><td data-label=\"How it is done\">Explanations are checked for consistency across similar cases and over time<\/td><\/tr><tr><td data-label=\"Check\">Drift monitoring<\/td><td data-label=\"How it is done\">Input and output distributions are tracked against thresholds that trigger review and retraining<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.<\/p><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-7\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Exposure, loss and capital<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">What machine learning in banking 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=\"scenario-pathways\"><h3 class=\"nx-h4\">Scenario pathways<\/h3><p class=\"nx-p nx-au-what\">How machine learning in banking could be stressed under the scenario families central banks and supervisors use.<\/p><div class=\"nx-au-fig\"><svg class=\"nx-au-svg\" viewBox=\"0 0 560 214\" role=\"img\" aria-label=\"Combined transition and physical pressure under three scenario families\" focusable=\"false\"><line class=\"nx-au-ax\" x1=\"46\" y1=\"190\" x2=\"450\" y2=\"190\"\/><line class=\"nx-au-ax\" x1=\"46\" y1=\"16\" x2=\"46\" y2=\"190\"\/><path class=\"nx-au-ln\" d=\"M46 155.2l80.8-38.3 80.8-13.9 80.8 7 80.8 10.4 80.8 8.7\"\/><text class=\"nx-au-t\" x=\"458\" y=\"133.1\" text-anchor=\"start\">Orderly<\/text><path class=\"nx-au-ac\" d=\"M46 155.2l80.8-3.5 80.8-13.9 80.8-73.1 80.8 10.5 80.8 19.1\"\/><text class=\"nx-au-t\" x=\"458\" y=\"98.3\" text-anchor=\"start\">Disorderly<\/text><path class=\"nx-au-ln nx-au-dash\" d=\"M46 155.2l80.8-7 80.8-10.4 80.8-13.9 80.8-20.9 80.8-27.8\"\/><text class=\"nx-au-t\" x=\"458\" y=\"79.16\" text-anchor=\"start\">Hot house world<\/text><text class=\"nx-au-t\" x=\"46\" y=\"206\" text-anchor=\"middle\">2025<\/text><text class=\"nx-au-t\" x=\"126.8\" y=\"206\" text-anchor=\"middle\">2030<\/text><text class=\"nx-au-t\" x=\"207.6\" y=\"206\" text-anchor=\"middle\">2035<\/text><text class=\"nx-au-t\" x=\"288.4\" y=\"206\" text-anchor=\"middle\">2040<\/text><text class=\"nx-au-t\" x=\"369.2\" y=\"206\" text-anchor=\"middle\">2045<\/text><text class=\"nx-au-t\" x=\"450\" y=\"206\" text-anchor=\"middle\">2050<\/text><text class=\"nx-au-t\" x=\"40\" y=\"33.4\" text-anchor=\"end\">high<\/text><text class=\"nx-au-t\" x=\"40\" y=\"172.6\" text-anchor=\"end\">low<\/text><\/svg><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Scenario family<\/th><th scope=\"col\">Transition risk<\/th><th scope=\"col\">Physical risk<\/th><th scope=\"col\">What a stress test asks<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Scenario family\">Orderly<\/td><td data-label=\"Transition risk\">Early, gradual policy<\/td><td data-label=\"Physical risk\">Lower<\/td><td data-label=\"What a stress test asks\">Can the book absorb a steady repricing?<\/td><\/tr><tr><td data-label=\"Scenario family\">Disorderly<\/td><td data-label=\"Transition risk\">Late, abrupt policy<\/td><td data-label=\"Physical risk\">Lower to medium<\/td><td data-label=\"What a stress test asks\">What breaks under a sudden repricing and credit migration?<\/td><\/tr><tr><td data-label=\"Scenario family\">Hot house world<\/td><td data-label=\"Transition risk\">Limited policy<\/td><td data-label=\"Physical risk\">High and rising<\/td><td data-label=\"What a stress test asks\">Where do physical losses concentrate in collateral and insurance?<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"nx-caption\"><span>Scenario families as defined by the NGFS; the curves are an illustrative combined pressure index, not NGFS variables or forecasts.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">NGFS climate scenarios \u00b7 Network for Greening the Financial System<\/span><span class=\"nx-chip\">IFRS S2 Climate-related Disclosures \u00b7 ISSB<\/span><span class=\"nx-chip\">TCFD recommendations \u00b7 Financial Stability Board TCFD<\/span><span class=\"nx-chip\">Principles for the effective management of climate-related financial risks \u00b7 Basel Committee on Banking Supervision<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" data-variant=\"band\" aria-labelledby=\"nx-s-8\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/topics\/credit-and-counterparty-risk\/\"><span>Credit and counterparty risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/topics\/bank-operational-resilience\/\"><span>Bank operational resilience<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/topics\/climate-and-nature-bank-risk\/\"><span>Climate and nature risk in banking<\/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\/banking-nexus\/topics\/payment-dependencies\/\"><span>Payment dependencies<\/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\/banking-nexus\/topics\/model-and-ai-risk\/\"><span>Model and AI risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/banking-nexus\/topics\/banking-and-real-economy\/\"><span>Banking and the real economy<\/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\/banking-nexus\/topics\/bank-governance\/\"><span>Bank 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\/banking-nexus\/topics\/housing-property-finance\/\"><span>Housing and property 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><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-variant=\"band\" aria-labelledby=\"nx-s-9\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/ai-autonomous-systems\/\">Artificial Intelligence and Autonomous Systems<\/a> stewards artificial intelligence and machine learning for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\">Canonical page: Artificial intelligence and machine learning<\/a><\/p><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-3\" data-hue=\"climate\" 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\" 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href=\"https:\/\/therisk.global\/ai-autonomous-systems\/stem\/ai-ml\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr><\/span><\/span><span class=\"nx-dest-title\">Machine learning behaviour<\/span><span class=\"nx-dest-why\">Artificial Intelligence and Autonomous Systems stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-11\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of artificial intelligence and machine learning in this field?<\/summary><div class=\"nx-answer\">Performance degrades when deployment conditions differ from training data. 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