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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>Assessing recorded credit-card payment default<\/h2>\n<p>Credit-risk teams need to understand both ranking performance and the consequences of a decision threshold. This example estimates the recorded default outcome from account limits, repayment history, bills and payments.<\/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>28<\/strong><span>Final input features<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>6,000<\/strong><span>Test observations<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.779<\/strong><span>Test ROC AUC<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Explore the model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/creditrisk-data-20260921.csv\">Download the data<\/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<span id=\"ApplicationType\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>Credit analysts can study the relationship between recorded client information and payment default. An internal benchmark supports model comparison; an operational lending policy requires separate assessment.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Payment outcome<\/h3><p>The positive label is default=1.<\/p><\/div><div class=\"ndb-value\"><h3>Client information<\/h3><p>Categorical fields expand into the final network input schema.<\/p><\/div><div class=\"ndb-value\"><h3>Policy assessment<\/h3><p>Review calibration, subgroup behaviour and decision costs before operational use.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Credit risk<\/span><span>Model validation<\/span><span>Portfolio analytics<\/span><\/div><div class=\"ndb-note\">Internal benchmark performance does not establish a suitable lending policy. Use an out-of-time population, review affordability and protected attributes, test calibration and subgroup errors, and retain human review. A model trained on existing customers may not represent new applicants.<\/div><\/section>\n<span id=\"DataSet\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The example uses the UCI Default of Credit Card Clients dataset. The saved project contains 30,000 records and expands categorical inputs before training. The positive class is default=1. Some encoded cells require mean replacement when the project is loaded.<\/p><p>The downloadable project, saved report and supplied source CSV define the exact version used here. Repository: <a href=\"https:\/\/archive.ics.uci.edu\/dataset\/350\/default+of+credit+card+clients\">original dataset\/source record<\/a>. <\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">Records<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>18000<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>6000<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>6000<\/td><\/tr><tr><th scope=\"row\">Unused<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Variable<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Encoding<\/th><th scope=\"col\">Unit<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">limit_balance<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">gender<\/th><td>Input<\/td><td>Binary<\/td><td>female; male<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">education_level<\/th><td>Input<\/td><td>Categorical<\/td><td>graduate_school; high_school; others; university<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">marital_status<\/th><td>Input<\/td><td>Categorical<\/td><td>married; others; single<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">repayment_status_lag_1<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">repayment_status_lag_2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">repayment_status_lag_3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">repayment_status_lag_4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">repayment_status_lag_5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">repayment_status_lag_6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">bill_state_amount_lag_1<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">bill_state_amount_lag_2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">bill_state_amount_lag_3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">bill_state_amount_lag_4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">bill_state_amount_lag_5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">bill_state_amount_lag_6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">payment_amount_lag_1<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">payment_amount_lag_2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">payment_amount_lag_3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">payment_amount_lag_4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">payment_amount_lag_5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">payment_amount_lag_6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">default<\/th><td>Target<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-creditrisk-t0-s27-20260921\"><div class=\"nd-chart-host\" id=\"nd-creditrisk-t0-s27-20260921\" role=\"img\" aria-label=\"default pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: default pie chart. Enable JavaScript to explore it.<\/p><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\":[77.9000015258789,22.100000381469727]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"default pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>default pie chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-creditrisk-t1-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-creditrisk-t1-s1-20260921\" role=\"img\" aria-label=\"default Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: default Pearson correlations chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"orientation\":\"h\",\"showlegend\":false,\"type\":\"bar\",\"x\":[-0.164000004529953,-0.14399999380111694,-0.14100000262260437,-0.12200000137090683,0.21400000154972076,0.23600000143051147,0.24699999392032623,0.26600000262260437,0.3050000071525574,0.3930000066757202],\"y\":[\"limit_balance\",\"payment_amount_lag_1\",\"payment_amount_lag_2\",\"payment_amount_lag_3\",\"repayment_status_lag_6\",\"repayment_status_lag_5\",\"repayment_status_lag_4\",\"repayment_status_lag_3\",\"repayment_status_lag_2\",\"repayment_status_lag_1\"]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"default Pearson correlations chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-1,1],\"title\":{\"text\":\"Correlation\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"autorange\":\"reversed\",\"categoryarray\":[\"limit_balance\",\"payment_amount_lag_1\",\"payment_amount_lag_2\",\"payment_amount_lag_3\",\"repayment_status_lag_6\",\"repayment_status_lag_5\",\"repayment_status_lag_4\",\"repayment_status_lag_3\",\"repayment_status_lag_2\",\"repayment_status_lag_1\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>default Pearson correlations chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><div class=\"ndb-note\">This is internal validation using the saved record-level split. Grouped or temporal independence has not been established. Mean across used rows, matching TabularDataset::scrub_missing_values; preprocessing is not isolated to training.<\/div><\/section>\n<span id=\"NeuralNetwork\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The final model has <strong>28 encoded input features<\/strong> and <strong>1 outputs<\/strong>. The following dimensions describe the final saved network.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><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>28<\/td><td>28<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>28<\/td><td>8<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>8<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output semantics: the sigmoid score increases toward <strong>1<\/strong>; 0 is the other class. Calibration has not been evaluated, so scores are not presented as calibrated probabilities.<\/p><figure class=\"ndb-architecture-figure\" data-model-stage=\"initial\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-creditrisk-t2-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-creditrisk-t2-s1-20260921.png\" alt=\"Assessing recorded credit-card payment default: initial Neural Designer architecture\"><\/a><figcaption>Initial architecture before selection. Diagram labels show original variables; categorical expansion and the numeric layer dimensions are documented in the model table.<\/figcaption><\/figure><\/section>\n<span id=\"TrainingStrategy\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses WeightedSquaredError with QuasiNewton. Training minimizes the recorded objective; the validation subset monitors generalization during fitting. The testing subset is used for the evaluation below.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-creditrisk-t3-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-creditrisk-t3-s2-20260921\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Quasi-Newton method error history. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training 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sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Quasi-Newton method error history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,208],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.8002],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Quasi-Newton method error history. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>209<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:02<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum validation error increases<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.58<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.61<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<span id=\"ModelSelection\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>The following search results are recorded in the saved report. Selection used the validation subset. These are the selected settings within the tested search; they do not establish a globally optimal model.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-creditrisk-t4-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-creditrisk-t4-s2-20260921\" role=\"img\" aria-label=\"Growing neurons training\/selection errors plot\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Growing neurons training\/selection errors plot. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"marker\":{\"color\":\"#529cc2\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10],\"y\":[0.613444983959198,0.5837420225143433,0.5720260143280029,0.5696370005607605,0.5695589780807495,0.5695919990539551,0.5692179799079895,0.5691030025482178,0.5746819972991943,0.5738909840583801]},{\"connectgaps\":false,\"line\":{\"color\":\"#e6cd5f\",\"dash\":\"solid\",\"width\":2},\"marker\":{\"color\":\"#e6cd5f\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Selection error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[1,2,3,4,5,6,7,8,9,10],\"y\":[0.6413249969482422,0.6118119955062866,0.6045349836349487,0.6029559969902039,0.6022700071334839,0.6016240119934082,0.6018940210342407,0.6015650033950806,0.6015679836273193,0.6020920276641846]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Growing neurons training\/selection errors plot\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[1,10],\"title\":{\"text\":\"Neurons number\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.7001000000000001],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Growing neurons training\/selection errors plot. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Growing neurons results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Optimal neurons number<\/th><td>8<\/td><\/tr><tr><th scope=\"row\">Optimum training error<\/th><td>0.5691<\/td><\/tr><tr><th scope=\"row\">Optimum selection error<\/th><td>0.6016<\/td><\/tr><tr><th scope=\"row\">Epochs number<\/th><td>10<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum neurons reached<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:29<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"ndb-architecture-figure\" data-model-stage=\"selected\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-creditrisk-t5-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-creditrisk-t5-s1-20260921.png\" alt=\"Assessing recorded credit-card payment default: selected Neural Designer architecture\"><\/a><figcaption>Final architecture used for testing. Diagram labels show original variables; categorical expansion and the numeric layer dimensions are documented in the model table.<\/figcaption><\/figure><p>A transparent test-set comparator is the majority-class rule, with accuracy 78.2%. This is a baseline for interpretation, not an alternative model fitted on the test labels.<\/p><\/section>\n<span id=\"TestingAnalysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The final classifier is evaluated on <strong>6,000 testing records<\/strong>. The confusion counts below were reproduced from the saved model. Rows are actual classes and columns are predicted classes. The decision threshold is 0.5 on the score for 1.<\/p><p>Test class prevalence is shown by the support counts. Accuracy is 76.8% and macro F1 is 0.694. ROC AUC is 0.779. The native ROC optimal threshold is descriptive of this test set and is not an independently validated operating policy.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Actual \/ predicted<\/th><th scope=\"col\">0<\/th><th scope=\"col\">1<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>3778<\/td><td>914<\/td><td>4692<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>479<\/td><td>829<\/td><td>1308<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Test cases<\/th><th scope=\"col\">Sensitivity \/ recall<\/th><th scope=\"col\">Specificity<\/th><th scope=\"col\">Precision \/ PPV<\/th><th scope=\"col\">F1<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">0<\/th><td>4692<\/td><td>80.5%<\/td><td>63.4%<\/td><td>88.7%<\/td><td>0.844<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>1308<\/td><td>63.4%<\/td><td>80.5%<\/td><td>47.6%<\/td><td>0.543<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-native-test-figures\"><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-creditrisk-t6-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-creditrisk-t6-s1-20260921\" role=\"img\" aria-label=\"ROC chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: ROC chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"fill\":\"tozeroy\",\"fillcolor\":\"rgba(82,156,194,0.300)\",\"line\":{\"color\":\"#ffffff\",\"dash\":\"solid\",\"width\":1},\"marker\":{\"color\":\"#529cc2\",\"size\":5,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Random 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sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Cumulative gain chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"Sample ratio\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"Rate\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Cumulative gain chart. Exported with Neural Designer from the saved task report. This native curve is expressed in positive\/negative rates. It is a discrimination view, not a validated fraction-of-customers campaign gain.<\/figcaption><\/figure><\/div><\/section>\n<span id=\"ModelDeployment\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Validated inputs \u2192 saved preprocessing \u2192 neural network \u2192 score or estimate \u2192 domain review. The ZIP contains the original project, source CSV, schema, test metrics and standalone interactive chart exports. The project hash in the schema identifies this exact version.<\/p><h3>Directional response at a fixed reference point<\/h3><details><summary>Inspect the saved reference point<\/summary><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Variable<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">1<\/th><td>limit_balance<\/td><td>50000<\/td><\/tr><tr><th scope=\"row\">2<\/th><td>gender<\/td><td>female<\/td><\/tr><tr><th scope=\"row\">3<\/th><td>education_level<\/td><td>graduate_school<\/td><\/tr><tr><th scope=\"row\">4<\/th><td>marital_status<\/td><td>single<\/td><\/tr><tr><th scope=\"row\">5<\/th><td>age<\/td><td>25<\/td><\/tr><tr><th scope=\"row\">6<\/th><td>repayment_status_lag_1<\/td><td>1<\/td><\/tr><tr><th scope=\"row\">7<\/th><td>repayment_status_lag_2<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">8<\/th><td>repayment_status_lag_3<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">9<\/th><td>repayment_status_lag_4<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">10<\/th><td>repayment_status_lag_5<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">11<\/th><td>repayment_status_lag_6<\/td><td>2<\/td><\/tr><tr><th scope=\"row\">12<\/th><td>bill_state_amount_lag_1<\/td><td>47054<\/td><\/tr><tr><th scope=\"row\">13<\/th><td>bill_state_amount_lag_2<\/td><td>48008<\/td><\/tr><tr><th scope=\"row\">14<\/th><td>bill_state_amount_lag_3<\/td><td>48913<\/td><\/tr><tr><th scope=\"row\">15<\/th><td>bill_state_amount_lag_4<\/td><td>49847<\/td><\/tr><tr><th scope=\"row\">16<\/th><td>bill_state_amount_lag_5<\/td><td>50840<\/td><\/tr><tr><th scope=\"row\">17<\/th><td>bill_state_amount_lag_6<\/td><td>49502<\/td><\/tr><tr><th scope=\"row\">18<\/th><td>payment_amount_lag_1<\/td><td>2000<\/td><\/tr><tr><th scope=\"row\">19<\/th><td>payment_amount_lag_2<\/td><td>2000<\/td><\/tr><tr><th scope=\"row\">20<\/th><td>payment_amount_lag_3<\/td><td>2000<\/td><\/tr><tr><th scope=\"row\">21<\/th><td>payment_amount_lag_4<\/td><td>1900<\/td><\/tr><tr><th scope=\"row\">22<\/th><td>payment_amount_lag_5<\/td><td>1900<\/td><\/tr><tr><th scope=\"row\">23<\/th><td>payment_amount_lag_6<\/td><td>1600<\/td><\/tr><\/tbody><\/table><\/div><\/details><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-creditrisk-t9-s7-20260921\"><div class=\"nd-chart-host\" id=\"nd-creditrisk-t9-s7-20260921\" role=\"img\" aria-label=\"default - repayment_status_lag_1 directional output\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: default &#8211; repayment_status_lag_1 directional output. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"shape\":\"spline\",\"width\":2},\"mode\":\"lines\",\"name\":\"default\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,0.019999999552965164,0.03999999910593033,0.05999999865889549,0.07999999821186066,0.10000000149011612,0.11999999731779099,0.14000000059604645,0.1599999964237213,0.18000000715255737,0.20000000298023224,0.2199999988079071,0.23999999463558197,0.25999999046325684,0.2800000011920929,0.30000001192092896,0.3199999928474426,0.3400000035762787,0.36000001430511475,0.3799999952316284,0.4000000059604645,0.41999998688697815,0.4399999976158142,0.46000000834465027,0.47999998927116394,0.5,0.5199999809265137,0.5400000214576721,0.5600000023841858,0.5799999833106995,0.6000000238418579,0.6200000047683716,0.6399999856948853,0.6600000262260437,0.6800000071525574,0.699999988079071,0.7200000286102295,0.7400000095367432,0.7599999904632568,0.7799999713897705,0.800000011920929,0.8199999928474426,0.8399999737739563,0.8600000143051147,0.8799999952316284,0.8999999761581421,0.9200000166893005,0.9399999976158142,0.9599999785423279,0.9800000190734863,1],\"y\":[0.6679999828338623,0.6679999828338623,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6669999957084656,0.6679999828338623,0.6679999828338623,0.6690000295639038,0.6690000295639038,0.6700000166893005,0.6710000038146973,0.6710000038146973,0.671999990940094,0.6729999780654907,0.6740000247955322,0.6759999990463257,0.6769999861717224,0.6779999732971191,0.6800000071525574,0.6819999814033508,0.6830000281333923,0.6850000023841858,0.6869999766349792,0.6890000104904175,0.6909999847412109,0.6940000057220459,0.6959999799728394,0.6990000009536743,0.7020000219345093,0.7039999961853027,0.7070000171661377,0.7099999785423279,0.7139999866485596,0.7170000076293945,0.7210000157356262,0.7239999771118164,0.7279999852180481,0.7319999933242798,0.7360000014305115,0.7400000095367432,0.7440000176429749,0.7480000257492065,0.7519999742507935,0.7570000290870667,0.7609999775886536]},{\"connectgaps\":false,\"marker\":{\"color\":\"#3c3c3c\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"default\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[1],\"y\":[0.7614219784736633]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"default - repayment_status_lag_1 directional output\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"repayment_status_lag_1\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"default\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>default &#8211; repayment_status_lag_1 directional output. Exported with Neural Designer from the saved task report. Other inputs are held at the saved reference point. This is a model response, not a causal effect.<\/figcaption><\/figure><div class=\"nd-model-calculator\" id=\"nd-model-creditrisk\"><h3>Explore the exported model<\/h3><p>This research demonstration runs locally in your browser. Values outside the training range are outside the validated domain and are rejected. A valid input range does not guarantee that a combination is physically or operationally plausible.<\/p><p>This is a research demonstration, not a validated decision policy.<\/p><div class=\"nd-model-fields\"><\/div><div class=\"nd-model-actions\"><button class=\"aui aui-button nd-model-calculate\" type=\"button\">Calculate<\/button><button class=\"aui aui-button aui-button--secondary nd-model-reset\" type=\"button\">Reset example<\/button><\/div><div class=\"nd-model-result\" role=\"status\" aria-live=\"polite\"><\/div><\/div><script data-noptimize=\"1\">(()=>{function Identity(x) {\n\treturn x;\n}\nfunction Sigmoid(x) {\n\tvar z = 1\/(1+Math.exp(-x));\n\treturn z;\n}\nfunction Tanh(x) {\n\treturn Math.tanh(x);\n}\nfunction calculate_outputs(inputs)\n{\n\tvar limit_balance = +inputs[0];\n\tvar gender = +inputs[1];\n\tvar graduate_school = +inputs[2];\n\tvar high_school = +inputs[3];\n\tvar others = +inputs[4];\n\tvar university = +inputs[5];\n\tvar married = +inputs[6];\n\tvar others_7 = +inputs[7];\n\tvar single = +inputs[8];\n\tvar age = +inputs[9];\n\tvar repayment_status_lag_1 = +inputs[10];\n\tvar repayment_status_lag_2 = +inputs[11];\n\tvar repayment_status_lag_3 = +inputs[12];\n\tvar repayment_status_lag_4 = +inputs[13];\n\tvar repayment_status_lag_5 = +inputs[14];\n\tvar repayment_status_lag_6 = +inputs[15];\n\tvar bill_state_amount_lag_1 = +inputs[16];\n\tvar bill_state_amount_lag_2 = +inputs[17];\n\tvar bill_state_amount_lag_3 = +inputs[18];\n\tvar bill_state_amount_lag_4 = +inputs[19];\n\tvar bill_state_amount_lag_5 = +inputs[20];\n\tvar bill_state_amount_lag_6 = +inputs[21];\n\tvar payment_amount_lag_1 = +inputs[22];\n\tvar payment_amount_lag_2 = +inputs[23];\n\tvar payment_amount_lag_3 = +inputs[24];\n\tvar payment_amount_lag_4 = +inputs[25];\n\tvar payment_amount_lag_5 = +inputs[26];\n\tvar payment_amount_lag_6 = +inputs[27];\n\n\tvar scaled_limit_balance = limit_balance*7.707663826e-06-1.290887237;\n\tvar scaled_gender = gender*2.0-1.0;\n\tvar scaled_graduate_school = graduate_school*2.0-1.0;\n\tvar scaled_high_school = high_school*2.0-1.0;\n\tvar scaled_others = others*2.0-1.0;\n\tvar scaled_university = university*2.0-1.0;\n\tvar scaled_married = married*2.0-1.0;\n\tvar scaled_others_7 = others_7*2.0-1.0;\n\tvar scaled_single = single*2.0-1.0;\n\tvar scaled_age = age*0.1084868237-3.849709272;\n\tvar scaled_repayment_status_lag_1 = repayment_status_lag_1*0.8898856044+0.01486108918;\n\tvar scaled_repayment_status_lag_2 = repayment_status_lag_2*0.835275352+0.1117322743;\n\tvar scaled_repayment_status_lag_3 = repayment_status_lag_3*0.8355335593+0.1388656795;\n\tvar scaled_repayment_status_lag_4 = repayment_status_lag_4*0.8553954363+0.1887575537;\n\tvar scaled_repayment_status_lag_5 = repayment_status_lag_5*0.8825113177+0.234924525;\n\tvar scaled_repayment_status_lag_6 = repayment_status_lag_6*0.8695728183+0.2531326413;\n\tvar scaled_bill_state_amount_lag_1 = bill_state_amount_lag_1*1.358059035e-05-0.6956426501;\n\tvar scaled_bill_state_amount_lag_2 = bill_state_amount_lag_2*1.405046896e-05-0.6909894347;\n\tvar scaled_bill_state_amount_lag_3 = bill_state_amount_lag_3*1.442021312e-05-0.6779389381;\n\tvar scaled_bill_state_amount_lag_4 = bill_state_amount_lag_4*1.554463051e-05-0.672505796;\n\tvar scaled_bill_state_amount_lag_5 = bill_state_amount_lag_5*1.644834265e-05-0.6630557179;\n\tvar scaled_bill_state_amount_lag_6 = bill_state_amount_lag_6*1.679187699e-05-0.652728796;\n\tvar scaled_payment_amount_lag_1 = payment_amount_lag_1*6.037663115e-05-0.3419484794;\n\tvar scaled_payment_amount_lag_2 = payment_amount_lag_2*4.340277883e-05-0.2569939494;\n\tvar scaled_payment_amount_lag_3 = payment_amount_lag_3*5.679784954e-05-0.2968074083;\n\tvar scaled_payment_amount_lag_4 = payment_amount_lag_4*6.383575965e-05-0.3080765009;\n\tvar scaled_payment_amount_lag_5 = payment_amount_lag_5*6.545231008e-05-0.3141311705;\n\tvar scaled_payment_amount_lag_6 = payment_amount_lag_6*5.625056292e-05-0.2933759391;\n\tvar dense_layer_1_output_0 = Tanh( -0.1703384072 + (-0.1719350517*scaled_limit_balance) + (0.005742039997*scaled_gender) + (0.1263073683*scaled_graduate_school) + (0.05654791743*scaled_high_school) + (0.00539406063*scaled_others) + (0.06206481159*scaled_university) + (0.05785036832*scaled_married) + (0.1441764832*scaled_others_7) + (-0.01164559089*scaled_single) + (-0.08744124323*scaled_age) + (-0.8354072571*scaled_repayment_status_lag_1) + (0.03067195415*scaled_repayment_status_lag_2) + (-0.02797871456*scaled_repayment_status_lag_3) + (-0.4088057578*scaled_repayment_status_lag_4) + (-0.556230247*scaled_repayment_status_lag_5) + (-0.7508054972*scaled_repayment_status_lag_6) + (0.06206445396*scaled_bill_state_amount_lag_1) + (0.103301093*scaled_bill_state_amount_lag_2) + (0.06006457284*scaled_bill_state_amount_lag_3) + (-0.1416473389*scaled_bill_state_amount_lag_4) + (-0.1080068499*scaled_bill_state_amount_lag_5) + (-0.1699907482*scaled_bill_state_amount_lag_6) + (0.2743175924*scaled_payment_amount_lag_1) + (0.1788400263*scaled_payment_amount_lag_2) + (-0.2374697328*scaled_payment_amount_lag_3) + (0.343696624*scaled_payment_amount_lag_4) + (0.1299770772*scaled_payment_amount_lag_5) + (0.2457406074*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_1 = Tanh( 0.04174626619 + (-0.1467123628*scaled_limit_balance) + (-0.05855319649*scaled_gender) + (-0.06470767409*scaled_graduate_school) + (-0.05676834658*scaled_high_school) + (0.1214003637*scaled_others) + (-0.09829314798*scaled_university) + (-0.08010468632*scaled_married) + (0.1070319116*scaled_others_7) + (-0.1300802678*scaled_single) + (0.1657074392*scaled_age) + (-0.002546871547*scaled_repayment_status_lag_1) + (-0.1094796062*scaled_repayment_status_lag_2) + (-0.07616688311*scaled_repayment_status_lag_3) + (-0.08076773584*scaled_repayment_status_lag_4) + (-0.05586838722*scaled_repayment_status_lag_5) + (-0.04191993922*scaled_repayment_status_lag_6) + (0.3159568608*scaled_bill_state_amount_lag_1) + (-0.09115111828*scaled_bill_state_amount_lag_2) + (-0.2697689235*scaled_bill_state_amount_lag_3) + (-0.01863502339*scaled_bill_state_amount_lag_4) + (0.04516657442*scaled_bill_state_amount_lag_5) + (-0.1069823802*scaled_bill_state_amount_lag_6) + (0.4961492717*scaled_payment_amount_lag_1) + (0.4420550168*scaled_payment_amount_lag_2) + (0.5609137416*scaled_payment_amount_lag_3) + (0.07514055818*scaled_payment_amount_lag_4) + (0.2716628611*scaled_payment_amount_lag_5) + (0.2413307428*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_2 = Tanh( -0.3638107777 + (-0.09410466254*scaled_limit_balance) + (-0.03161627799*scaled_gender) + (0.1735246927*scaled_graduate_school) + (0.1047581732*scaled_high_school) + (0.2339177281*scaled_others) + (0.2054793537*scaled_university) + (0.09707728028*scaled_married) + (0.2577515841*scaled_others_7) + (0.1257124096*scaled_single) + (0.009405366145*scaled_age) + (-0.5575642586*scaled_repayment_status_lag_1) + (0.02150723897*scaled_repayment_status_lag_2) + (-0.05220260471*scaled_repayment_status_lag_3) + (0.04550009221*scaled_repayment_status_lag_4) + (0.03024943918*scaled_repayment_status_lag_5) + (0.04658445343*scaled_repayment_status_lag_6) + (-0.5783439279*scaled_bill_state_amount_lag_1) + (-0.4264880717*scaled_bill_state_amount_lag_2) + (-0.272127986*scaled_bill_state_amount_lag_3) + (-0.1096401438*scaled_bill_state_amount_lag_4) + (-0.08273626864*scaled_bill_state_amount_lag_5) + (-0.08307032287*scaled_bill_state_amount_lag_6) + (-0.07951996475*scaled_payment_amount_lag_1) + (-0.08916959167*scaled_payment_amount_lag_2) + (0.2010208666*scaled_payment_amount_lag_3) + (-0.1242168844*scaled_payment_amount_lag_4) + (0.02525001578*scaled_payment_amount_lag_5) + (0.0392421335*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_3 = Tanh( 0.504580617 + (-0.02474625967*scaled_limit_balance) + (0.004781670403*scaled_gender) + (-0.228230685*scaled_graduate_school) + (-0.2533361614*scaled_high_school) + (-0.3418579698*scaled_others) + (-0.2253333479*scaled_university) + (-0.04838841408*scaled_married) + (-0.2851634026*scaled_others_7) + (-0.1105164513*scaled_single) + (-0.1038480178*scaled_age) + (-1.482253075*scaled_repayment_status_lag_1) + (0.1879185438*scaled_repayment_status_lag_2) + (0.1278330535*scaled_repayment_status_lag_3) + (-0.03340320289*scaled_repayment_status_lag_4) + (-0.02642605454*scaled_repayment_status_lag_5) + (-0.04421231151*scaled_repayment_status_lag_6) + (-0.2575502396*scaled_bill_state_amount_lag_1) + (-0.0922504887*scaled_bill_state_amount_lag_2) + (0.234713003*scaled_bill_state_amount_lag_3) + (0.02311123163*scaled_bill_state_amount_lag_4) + (-0.0628061071*scaled_bill_state_amount_lag_5) + (0.06847272068*scaled_bill_state_amount_lag_6) + (0.02709375694*scaled_payment_amount_lag_1) + (-0.081633389*scaled_payment_amount_lag_2) + (-0.03817218915*scaled_payment_amount_lag_3) + (-0.04036980122*scaled_payment_amount_lag_4) + (-0.07091914862*scaled_payment_amount_lag_5) + (0.1175367907*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_4 = Tanh( -0.2697482407 + (-0.4864783585*scaled_limit_balance) + (0.1203664467*scaled_gender) + (-0.006958180107*scaled_graduate_school) + (0.1647281945*scaled_high_school) + (0.1545532942*scaled_others) + (0.1729439348*scaled_university) + (0.06190782785*scaled_married) + (0.1964641511*scaled_others_7) + (-0.02506233193*scaled_single) + (-0.1613462716*scaled_age) + (0.3231358528*scaled_repayment_status_lag_1) + (0.3908732533*scaled_repayment_status_lag_2) + (0.4817520976*scaled_repayment_status_lag_3) + (0.3684107959*scaled_repayment_status_lag_4) + (0.05640756339*scaled_repayment_status_lag_5) + (0.3135129809*scaled_repayment_status_lag_6) + (0.1444011182*scaled_bill_state_amount_lag_1) + (0.139229387*scaled_bill_state_amount_lag_2) + (0.08194351941*scaled_bill_state_amount_lag_3) + (0.03485632688*scaled_bill_state_amount_lag_4) + (0.04691983387*scaled_bill_state_amount_lag_5) + (-0.02261438593*scaled_bill_state_amount_lag_6) + (0.1301639229*scaled_payment_amount_lag_1) + (-0.08220054209*scaled_payment_amount_lag_2) + (0.09290168434*scaled_payment_amount_lag_3) + (-0.0174710229*scaled_payment_amount_lag_4) + (-0.05191767216*scaled_payment_amount_lag_5) + (0.1502269506*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_5 = Tanh( -0.04950069636 + (0.2000921667*scaled_limit_balance) + (-0.01209240593*scaled_gender) + (-0.02722727694*scaled_graduate_school) + (-0.06965257227*scaled_high_school) + (0.2072776854*scaled_others) + (0.1153519973*scaled_university) + (-0.1138276979*scaled_married) + (0.0550060831*scaled_others_7) + (0.06565377861*scaled_single) + (-0.406065762*scaled_age) + (-0.1779979914*scaled_repayment_status_lag_1) + (-0.05995433405*scaled_repayment_status_lag_2) + (-0.08895361423*scaled_repayment_status_lag_3) + (-0.1298963577*scaled_repayment_status_lag_4) + (-0.216358304*scaled_repayment_status_lag_5) + (-0.1366389841*scaled_repayment_status_lag_6) + (0.0678518489*scaled_bill_state_amount_lag_1) + (-0.209720239*scaled_bill_state_amount_lag_2) + (0.04484992102*scaled_bill_state_amount_lag_3) + (0.01018781867*scaled_bill_state_amount_lag_4) + (-0.04262468591*scaled_bill_state_amount_lag_5) + (-0.05046210811*scaled_bill_state_amount_lag_6) + (0.144926697*scaled_payment_amount_lag_1) + (-0.02027705684*scaled_payment_amount_lag_2) + (-0.0528457053*scaled_payment_amount_lag_3) + (0.2680845559*scaled_payment_amount_lag_4) + (0.005844171159*scaled_payment_amount_lag_5) + (0.0764356479*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_6 = Tanh( 0.0004543288378 + (-0.09173609316*scaled_limit_balance) + (-0.01013007574*scaled_gender) + (-0.07916975021*scaled_graduate_school) + (-0.07126069814*scaled_high_school) + (0.0551170744*scaled_others) + (-0.01053193957*scaled_university) + (-0.03821253031*scaled_married) + (-0.09064051509*scaled_others_7) + (-0.05682353675*scaled_single) + (0.08816954494*scaled_age) + (-0.09482481331*scaled_repayment_status_lag_1) + (0.04556471482*scaled_repayment_status_lag_2) + (0.01911812462*scaled_repayment_status_lag_3) + (-0.09892736375*scaled_repayment_status_lag_4) + (0.06422761083*scaled_repayment_status_lag_5) + (0.03900294751*scaled_repayment_status_lag_6) + (-0.01905985922*scaled_bill_state_amount_lag_1) + (0.07239833474*scaled_bill_state_amount_lag_2) + (0.05638734996*scaled_bill_state_amount_lag_3) + (-0.07432761043*scaled_bill_state_amount_lag_4) + (-0.05636302009*scaled_bill_state_amount_lag_5) + (-0.01315162983*scaled_bill_state_amount_lag_6) + (0.04690013081*scaled_payment_amount_lag_1) + (0.09558840841*scaled_payment_amount_lag_2) + (0.04632527754*scaled_payment_amount_lag_3) + (-0.09521788359*scaled_payment_amount_lag_4) + (0.02079833671*scaled_payment_amount_lag_5) + (0.04728050157*scaled_payment_amount_lag_6) );\n\tvar dense_layer_1_output_7 = Tanh( -0.0001675651292 + (0.06090760231*scaled_limit_balance) + (0.02058055624*scaled_gender) + (-0.02438169159*scaled_graduate_school) + (-0.08936343342*scaled_high_school) + (0.07554669678*scaled_others) + (-0.08511372656*scaled_university) + (0.03555572405*scaled_married) + (-0.003260326106*scaled_others_7) + (0.07027872652*scaled_single) + (0.05367604271*scaled_age) + (0.0365337804*scaled_repayment_status_lag_1) + (-0.08855418861*scaled_repayment_status_lag_2) + (-0.02495627105*scaled_repayment_status_lag_3) + (-0.06144825369*scaled_repayment_status_lag_4) + (0.02462837286*scaled_repayment_status_lag_5) + (0.06560153514*scaled_repayment_status_lag_6) + (-0.03608550504*scaled_bill_state_amount_lag_1) + (0.01478804275*scaled_bill_state_amount_lag_2) + (-0.0341716595*scaled_bill_state_amount_lag_3) + (-0.0108500747*scaled_bill_state_amount_lag_4) + (-0.06093072891*scaled_bill_state_amount_lag_5) + (-0.0484293364*scaled_bill_state_amount_lag_6) + (0.0388558507*scaled_payment_amount_lag_1) + (-0.06534893811*scaled_payment_amount_lag_2) + (0.04948246479*scaled_payment_amount_lag_3) + (-0.08253884315*scaled_payment_amount_lag_4) + (0.03861832246*scaled_payment_amount_lag_5) + (-0.06643015891*scaled_payment_amount_lag_6) );\n\tvar default_ = Sigmoid( 1.005555868 + (0.7562320232*dense_layer_1_output_0) + (-0.8625182509*dense_layer_1_output_1) + (0.8557521105*dense_layer_1_output_2) + (-1.337002397*dense_layer_1_output_3) + (0.9115133286*dense_layer_1_output_4) + (-0.6847590208*dense_layer_1_output_5) + (0.08586792648*dense_layer_1_output_6) + (-0.05278702453*dense_layer_1_output_7) );\n\tvar out = [];\n\tout.push(default_);\n\n\treturn out;\n}\nconst cfg={\"fields\":[{\"name\":\"limit_balance\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":10000.0,\"max\":1000000.0,\"value\":90000.0,\"unit\":\"\"},{\"name\":\"gender\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"female\",\"male\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"education_level\",\"n\":4,\"type\":\"Categorical\",\"categories\":[\"graduate_school\",\"high_school\",\"others\",\"university\"],\"min\":0.0,\"max\":1.0,\"value\":3,\"unit\":\"\"},{\"name\":\"marital_status\",\"n\":3,\"type\":\"Categorical\",\"categories\":[\"married\",\"others\",\"single\"],\"min\":0.0,\"max\":1.0,\"value\":2,\"unit\":\"\"},{\"name\":\"age\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":21.0,\"max\":79.0,\"value\":34.0,\"unit\":\"\"},{\"name\":\"repayment_status_lag_1\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-2.0,\"max\":8.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"repayment_status_lag_2\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-2.0,\"max\":8.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"repayment_status_lag_3\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-2.0,\"max\":8.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"repayment_status_lag_4\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-2.0,\"max\":8.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"repayment_status_lag_5\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-2.0,\"max\":8.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"repayment_status_lag_6\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-2.0,\"max\":8.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"bill_state_amount_lag_1\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-165580.0,\"max\":964511.0,\"value\":29239.0,\"unit\":\"\"},{\"name\":\"bill_state_amount_lag_2\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-69777.0,\"max\":983931.0,\"value\":14027.0,\"unit\":\"\"},{\"name\":\"bill_state_amount_lag_3\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-157264.0,\"max\":1664090.0,\"value\":13559.0,\"unit\":\"\"},{\"name\":\"bill_state_amount_lag_4\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-170000.0,\"max\":891586.0,\"value\":14331.0,\"unit\":\"\"},{\"name\":\"bill_state_amount_lag_5\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-81334.0,\"max\":927171.0,\"value\":14948.0,\"unit\":\"\"},{\"name\":\"bill_state_amount_lag_6\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":-339603.0,\"max\":961664.0,\"value\":15549.0,\"unit\":\"\"},{\"name\":\"payment_amount_lag_1\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":873552.0,\"value\":1518.0,\"unit\":\"\"},{\"name\":\"payment_amount_lag_2\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":1684260.0,\"value\":1500.0,\"unit\":\"\"},{\"name\":\"payment_amount_lag_3\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":896040.0,\"value\":1000.0,\"unit\":\"\"},{\"name\":\"payment_amount_lag_4\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":621000.0,\"value\":1000.0,\"unit\":\"\"},{\"name\":\"payment_amount_lag_5\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":426529.0,\"value\":1000.0,\"unit\":\"\"},{\"name\":\"payment_amount_lag_6\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":528666.0,\"value\":5000.0,\"unit\":\"\"}],\"output_names\":[\"1 score\"],\"reference\":[0.32053420548030587]},root=document.getElementById(\"nd-model-creditrisk\");if(!root)return;const grid=root.querySelector(\".nd-model-fields\"),result=root.querySelector(\".nd-model-result\"),controls=[];cfg.fields.forEach((f,i)=>{const wrap=document.createElement(\"div\");wrap.className=\"nd-model-field\";const label=document.createElement(\"label\"),id=root.id+\"-\"+i;label.htmlFor=id;label.textContent=f.name.replaceAll(\"_\",\" \")+(f.unit?\" (\"+f.unit+\")\":\"\");let input;if(f.categories?.length){input=document.createElement(\"select\");f.categories.forEach((name,k)=>{const option=document.createElement(\"option\");option.value=String(k);option.textContent=name;input.appendChild(option);});}else{input=document.createElement(\"input\");input.type=\"number\";input.step=\"any\";input.min=String(f.min);input.max=String(f.max);}input.id=id;input.value=String(f.value);wrap.append(label,input);grid.appendChild(wrap);controls.push(input);});const calculate=()=>{const inputs=[];let valid=true;cfg.fields.forEach((f,i)=>{const el=controls[i],v=Number(el.value);let bad=el.value.trim()===\"\"||!Number.isFinite(v);if(!f.categories?.length)bad=bad||v<f.min||v>f.max;el.setAttribute(\"aria-invalid\",bad?\"true\":\"false\");if(bad)valid=false;if(f.n>1){for(let k=0;k<f.n;k++)inputs.push(k===v?1:0);}else inputs.push(v);});if(!valid){result.textContent=\"Enter a valid value within every displayed input range.\";return;}let outputs=calculate_outputs(inputs);if(outputs.some(v=>!Number.isFinite(v))){result.textContent=\"The model could not evaluate these inputs.\";return;}result.textContent=outputs.map((v,i)=>cfg.output_names[i]+\": \"+Number(v.toPrecision(7))).join(\"\\n\");root.dataset.outputs=JSON.stringify(outputs);};root.querySelector(\".nd-model-calculate\").addEventListener(\"click\",calculate);root.querySelector(\".nd-model-reset\").addEventListener(\"click\",()=>{controls.forEach((el,i)=>el.value=String(cfg.fields[i].value));calculate();});calculate();})();<\/script><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/creditrisk-neural-designer-20260921.zip\">Download project and exports (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/creditrisk-data-20260921.csv\">Download source data (CSV)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/creditrisk-schema-20260921.zip\">Download input schema (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Reproduce with Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Internal benchmark performance does not establish a suitable lending policy. Use an out-of-time population, review affordability and protected attributes, test calibration and subgroup errors, and retain human review. A model trained on existing customers may not represent new applicants.<\/p><p>No external validation or independent calibration study is included. Preprocessing statistics and model choices should be refitted within a prospective or grouped validation design. Correlations and directional responses describe associations, not causes. Human review is required before an operational decision.<\/p><p>Confidence intervals, subgroup performance, calibration curves and decision-cost validation are not established by these tasks. Predictive values apply to the observed test class distribution and may change when prevalence shifts.<\/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\">Dataset source and provenance<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/creditrisk-neural-designer-20260921.zip\">Neural Designer project and saved task evidence, 21 September 2026<\/a>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">Neural Designer testing-analysis documentation<\/a>.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":13,"featured_media":2358,"template":"","categories":[29],"tags":[47],"class_list":["post-3480","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-finance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Assess the risk of default payments using machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to predict the risk of default payments on consumer credits for a bank&#039;s customers.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/credit-risk-management\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Credit Risk Assessment machine learning example\" \/>\n<meta property=\"og:description\" content=\"The objective of this example is to predict customer&#039;s default payments in a bank. 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