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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>Prioritizing bank customer retention<\/h2>\n<p>A retention team needs to identify customers who may leave before committing resources to a campaign. This example trains a classifier on customer profiles and evaluates its ability to rank recorded churn outcomes.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>10,000<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>12<\/strong><span>Final input features<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>2,000<\/strong><span>Test observations<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.848<\/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\/bank-churn-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>Retention teams need to distinguish a churn score from the benefit of a retention offer. This example studies the former, using recorded customer and account characteristics.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Customer context<\/h3><p>Compare account, tenure and demographic patterns.<\/p><\/div><div class=\"ndb-value\"><h3>Churn discrimination<\/h3><p>Inspect missed churners as well as correctly classified customers.<\/p><\/div><div class=\"ndb-value\"><h3>Campaign evaluation<\/h3><p>Measure the effect of an offer separately before assigning a campaign budget.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Customer retention<\/span><span>Banking analytics<\/span><span>Campaign operations<\/span><\/div><div class=\"ndb-note\">A churn score does not establish which intervention will work. Evaluate on later customer cohorts, audit protected and proxy attributes, and measure incremental retention through a controlled campaign before claiming financial benefit.<\/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 supplied benchmark contains customer account records. It supports a retrospective classification exercise; the project does not establish a future observation horizon or the causal effect of a retention offer.<\/p><p>The downloadable project, saved report and supplied source CSV define the exact version used here. The project does not identify an independently verified external data record. <\/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>6000<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>2000<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>2000<\/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\">credit_score<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">country<\/th><td>Input<\/td><td>Categorical<\/td><td>France; Germany; Spain<\/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\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">tenure<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">balance<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">products_number<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">credit_card<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">active_member<\/th><td>Input<\/td><td>Binary<\/td><td>0; 1<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">estimated_salary<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">churn<\/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-bank-churn-t0-s15-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-churn-t0-s15-20260921\" role=\"img\" aria-label=\"churn pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: churn 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\":[79.5999984741211,20.399999618530273]}],\"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\":\"churn pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>churn 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-bank-churn-t1-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-churn-t1-s1-20260921\" role=\"img\" aria-label=\"churn Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: churn 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\":[-0.15600000321865082,-0.11900000274181366,-0.10700000077486038,-0.027899999171495438,-0.0142000000923872,-0.007139999885112047,0.012199999764561653,0.11699999868869781,0.17399999499320984,0.26499998569488525],\"y\":[\"active_member\",\"products_number\",\"gender\",\"credit_score\",\"tenure\",\"credit_card\",\"estimated_salary\",\"balance\",\"country\",\"age\"]}],\"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\":\"churn 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\":[\"active_member\",\"products_number\",\"gender\",\"credit_score\",\"tenure\",\"credit_card\",\"estimated_salary\",\"balance\",\"country\",\"age\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>churn 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.<\/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>12 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>12<\/td><td>12<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>12<\/td><td>4<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>4<\/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-bank-churn-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-bank-churn-t2-s1-20260921.png\" alt=\"Prioritizing bank customer retention: 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-bank-churn-t3-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-churn-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\":[0,146],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.8001],\"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>147<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:00<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.486<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.478<\/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-bank-churn-t4-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-churn-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\":[0.5673909783363342,0.5346540212631226,0.454925000667572,0.4278770089149475,0.4238109886646271,0.424017995595932,0.4237990081310272,0.4236670136451721,0.4236060082912445,0.4235680103302002]},{\"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\":[0.5290660262107849,0.494174987077713,0.4366919994354248,0.4292759895324707,0.42941799759864807,0.4293600022792816,0.429874986410141,0.4297870099544525,0.42980098724365234,0.4299969971179962]}],\"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\":[0,0.6001000000000001],\"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>4<\/td><\/tr><tr><th scope=\"row\">Optimum training error<\/th><td>0.4279<\/td><\/tr><tr><th scope=\"row\">Optimum selection error<\/th><td>0.4293<\/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>00:00:14<\/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-bank-churn-t7-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-bank-churn-t7-s1-20260921.png\" alt=\"Prioritizing bank customer retention: 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 77.7%. 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>2,000 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 77.8% and macro F1 is 0.721. ROC AUC is 0.848. 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>1228<\/td><td>326<\/td><td>1554<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>119<\/td><td>327<\/td><td>446<\/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>1554<\/td><td>79.0%<\/td><td>73.3%<\/td><td>91.2%<\/td><td>0.847<\/td><\/tr><tr><th scope=\"row\">1<\/th><td>446<\/td><td>73.3%<\/td><td>79.0%<\/td><td>50.1%<\/td><td>0.595<\/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-bank-churn-t5-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-churn-t5-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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chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"False Positive Rate (1-specificity)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"True Positive Rate (sensitivity)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>ROC 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-bank-churn-t8-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-bank-churn-t8-s1-20260921\" role=\"img\" aria-label=\"Cumulative gain chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Cumulative gain chart. 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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><div class=\"nd-model-calculator\" id=\"nd-model-bank-churn\"><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 credit_score = +inputs[0];\n\tvar France = +inputs[1];\n\tvar Germany = +inputs[2];\n\tvar Spain = +inputs[3];\n\tvar gender = +inputs[4];\n\tvar age = +inputs[5];\n\tvar tenure = +inputs[6];\n\tvar balance = +inputs[7];\n\tvar products_number = +inputs[8];\n\tvar credit_card = +inputs[9];\n\tvar active_member = +inputs[10];\n\tvar estimated_salary = +inputs[11];\n\n\tvar scaled_credit_score = credit_score*0.01034677215-6.730875015;\n\tvar scaled_France = France*2.0-1.0;\n\tvar scaled_Germany = Germany*2.0-1.0;\n\tvar scaled_Spain = Spain*2.0-1.0;\n\tvar scaled_gender = gender*2.0-1.0;\n\tvar scaled_age = age*0.09535251558-3.711291313;\n\tvar scaled_tenure = tenure*0.3457790911-1.733321548;\n\tvar scaled_balance = balance*1.602671909e-05-1.225816369;\n\tvar scaled_products_number = products_number*1.71937418-2.630986452;\n\tvar scaled_credit_card = credit_card*2.0-1.0;\n\tvar scaled_active_member = active_member*2.0-1.0;\n\tvar scaled_estimated_salary = estimated_salary*1.738900573e-05-1.740465641;\n\tvar dense_layer_1_output_0 = Tanh( -0.6899208426 + (-0.04165093973*scaled_credit_score) + (0.2386642992*scaled_France) + (0.2454694808*scaled_Germany) + (0.2057688832*scaled_Spain) + 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(0.9233366251*scaled_age) + (0.08129594475*scaled_tenure) + (-0.0338623561*scaled_balance) + (-0.1993920356*scaled_products_number) + (0.02793234773*scaled_credit_card) + (0.5340242386*scaled_active_member) + (-0.006474196911*scaled_estimated_salary) );\n\tvar dense_layer_1_output_3 = Tanh( -0.618489027 + (-0.04887460545*scaled_credit_score) + (0.195061788*scaled_France) + (0.1152120307*scaled_Germany) + (0.3091279864*scaled_Spain) + (-0.1099673212*scaled_gender) + (0.5342516899*scaled_age) + (0.007883412763*scaled_tenure) + (-0.1095655411*scaled_balance) + (0.6721675396*scaled_products_number) + (-0.07581474632*scaled_credit_card) + (-0.09483419359*scaled_active_member) + (0.02327090502*scaled_estimated_salary) );\n\tvar churn = Sigmoid( -0.5926662087 + (3.01634717*dense_layer_1_output_0) + (-1.379412413*dense_layer_1_output_1) + (-2.819971561*dense_layer_1_output_2) + (2.569300652*dense_layer_1_output_3) );\n\tvar out = [];\n\tout.push(churn);\n\n\treturn out;\n}\nconst 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score\"],\"reference\":[0.9323214589899347]},root=document.getElementById(\"nd-model-bank-churn\");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\/bank-churn-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\/bank-churn-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\/bank-churn-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>A churn score does not establish which intervention will work. Evaluate on later customer cohorts, audit protected and proxy attributes, and measure incremental retention through a controlled campaign before claiming financial benefit.<\/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:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/bank-churn-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":2613,"template":"","categories":[29],"tags":[47],"class_list":["post-3463","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>Reduce customer churn in a bank using machine learning<\/title>\n<meta name=\"description\" content=\"Practice with this machine learning example how to predict customer churn in a bank and take measures to reduce it.\" \/>\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\/bank-churn\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bank churn prediction machine learning example\" \/>\n<meta property=\"og:description\" content=\"As we know, it is much more expensive to sign in a new client than keeping an existing one. 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