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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Model next-month payment default<\/h2>\n<p>Use the UCI Default of Credit Card Clients dataset to classify the next monthly payment outcome. The saved network uses 23 inputs and is evaluated on 6,000 held-out customer records.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>30,000<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>23<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>6,000<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>1<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-creditdefault-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Business decision<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>Credit analysts can study how historical repayment and account descriptors associate with next-month default. The example demonstrates benchmark evaluation and error trade-offs; it does not estimate fraud or prescribe a lending decision.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Payment outcome<\/h3><p>Keep next-month default distinct from transaction fraud.<\/p><\/div><div class=\"ndb-value-card\"><h3>Error trade-off<\/h3><p>Review default sensitivity and false-positive costs.<\/p><\/div><div class=\"ndb-value-card\"><h3>Portfolio validation<\/h3><p>Compare performance on later customer cohorts.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Credit analytics<\/span><span>Risk modelling<\/span><span>Model validation<\/span><\/div><div class=\"ndb-note\">Historical customer-record classification for research and model evaluation.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The dataset contains 30,000 credit-card customer records. The identifier is excluded, and the target default_payment_next_month uses 1 for default and 0 for no default.<\/p><p>Source: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/350\/default+of+credit+card+clients\">Default of Credit Card Clients<\/a>. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>customer record<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>30,000<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>24<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>23<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>1<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>18000<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>6000<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>6000<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">LIMIT_BAL<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">SEX<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">EDUCATION<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">MARRIAGE<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">AGE<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_0<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">BILL_AMT1<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">BILL_AMT2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">BILL_AMT3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">BILL_AMT4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">BILL_AMT5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">BILL_AMT6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_AMT1<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_AMT2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_AMT3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_AMT4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_AMT5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">PAY_AMT6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">default_payment_next_month<\/th><td>Target<\/td><td>Binary<\/td><td>0, 1<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-creditdefault-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-creditdefault-t0-s2-20261006\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-creditdefault-t0-s2-20261006.png\" alt=\"Target class distribution pie chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"0\",\"1\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[77.87999725341797,22.1200008392334]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"height\":500}}<\/script><\/div><figcaption>Target class distribution pie chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 23 encoded inputs and 1 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>23<\/td><td>23<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>23<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. For binary evaluation, the saved positive class is <strong>1<\/strong>.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-creditdefault-t3-s1-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-creditdefault-t3-s1-20261006.png\" alt=\"Credit card payment default prediction \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses QuasiNewton with WeightedSquaredError.<\/p><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>237<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:02<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.568<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.591<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-creditdefault-t4-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-creditdefault-t4-s2-20261006\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><img decoding=\"async\" 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><p>On this test subset a majority-class baseline would correctly classify 78.07% of the records. This baseline does not detect both classes and is not an optimized model.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 6000 source records.<\/p><p>ROC AUC describes ranking on this testing subset. Any optimal threshold shown in the saved ROC report was selected descriptively on that same subset; it is not an independently validated operating policy.<\/p><p>These operating-point metrics are calculated from the saved confusion counts at threshold <strong>0.5<\/strong>. The positive-label coding is stated in the model section.<\/p><h3>Confusion table<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>792 (13.2%)<\/td><td>524 (8.7%)<\/td><td>1316 (21.9%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>954 (15.9%)<\/td><td>3730 (62.2%)<\/td><td>4684 (78.1%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>1746 (29.1%)<\/td><td>4254 (70.9%)<\/td><td>6000 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>6000<\/td><\/tr><tr><th scope=\"row\">Positive cases<\/th><td>1316<\/td><\/tr><tr><th scope=\"row\">Positive prevalence<\/th><td>21.93%<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>75.37%<\/td><\/tr><tr><th scope=\"row\">Sensitivity \/ recall<\/th><td>60.18%<\/td><\/tr><tr><th scope=\"row\">Specificity<\/th><td>79.63%<\/td><\/tr><tr><th scope=\"row\">Precision \/ PPV<\/th><td>45.36%<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>0.517<\/td><\/tr><\/tbody><\/table><\/div><h3>Area under curve<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Area under curve<\/th><td>0.772<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-creditdefault-t5-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-creditdefault-t5-s1-20261006\" role=\"img\" aria-label=\"ROC chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-creditdefault-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Internal row-level evaluation does not establish current portfolio performance, calibration or fair treatment across groups. Historical selection, policy and economic changes may shift the result. Human review and independent temporal, subgroup and calibration evaluation are required before individual lending decisions.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/350\/default+of+credit+card+clients\">Default of Credit Card Clients<\/a>. I-Cheng Yeh. 10.24432\/C55S3H<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. 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