{"id":3466,"date":"2023-08-31T11:13:00","date_gmt":"2023-08-31T11:13:00","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/bankruptcy-prevention\/"},"modified":"2026-02-11T15:56:23","modified_gmt":"2026-02-11T14:56:23","slug":"bankruptcy-prevention","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/bankruptcy-prevention\/","title":{"rendered":"Assess the risk of company bankruptcy through machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3466\" class=\"elementor elementor-3466\" data-elementor-post-type=\"learning\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-69f684bc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"69f684bc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-57c53d87\" data-id=\"57c53d87\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7b08b908 elementor-widget elementor-widget-text-editor\" data-id=\"7b08b908\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><p data-start=\"110\" data-end=\"345\">This example aims to build a machine learning model to assess bankruptcy risk based on qualitative parameters provided by experts.<\/p><p data-start=\"347\" data-end=\"597\">In general,\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/solutions\/risk-assessment\">Risk assessment<\/a>\u00a0i\u00a0involves identifying hazards and risk factors that could potentially harm a business, including the possibility of bankruptcy. Therefore, understanding these factors is essential for proactive financial management.<\/p><p data-start=\"599\" data-end=\"874\" data-is-last-node=\"\" data-is-only-node=\"\">More specifically, bankruptcy is a legal proceeding that occurs when a person or business becomes unable to repay outstanding debts. Consequently, early detection of financial distress can help organizations implement corrective strategies before reaching that stage.<\/p><\/section><h3>Contents<\/h3><ol><li><a href=\"#ApplicationType\">Application type<\/a>.<\/li><li><a href=\"#DataSet\">Data set<\/a>.<\/li><li><a href=\"#NeuralNetwork\">Neural network<\/a>.<\/li><li><a href=\"#TrainingStrategy\">Training strategy<\/a>.<\/li><li><a href=\"#ModelSelection\">Model selection<\/a>.<\/li><li><a href=\"#TestingAnalysis\">Testing analysis<\/a>.<\/li><li><a href=\"#ModelDeployment\">Model deployment<\/a>.<\/li><\/ol><p>This example is solved with <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>. In order to follow this example step by step, you can use the <a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">free trial<\/a>.<\/p><section><h2>1. Application type<\/h2><p data-start=\"0\" data-end=\"177\">This is a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#Classification\">classification<\/a> project, since the target variable is binary: bankruptcy or non-bankruptcy. Therefore, the model must distinguish between two possible outcomes.<\/p><p data-start=\"179\" data-end=\"433\" data-is-last-node=\"\" data-is-only-node=\"\">In this context, the goal is to estimate the probability that a business will go bankrupt based on various qualitative and financial features. As a result, the model provides a risk score that supports early decision-making and preventive action.<\/p><\/section><section><h2>2. Data set<\/h2><p>The data set contains information to create our model. We need to configure three things:<\/p><ul><li>Data source.<\/li><li>Variables.<\/li><li>Instances.<\/li><\/ul><p>The data file used for this example is <a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/bankruptcy-prevention.csv\">bankruptcy-prevention.csv<\/a>, which contains 7 features about 250 companies.<\/p><p>The data set includes the following <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables<\/a>:<\/p><ul><li><b>industrial_risk:<\/b> 0=low risk, 0.5=medium risk, 1=high risk.<\/li><li><b>management_risk:<\/b> 0=low risk, 0.5=medium risk, 1=high risk.<\/li><li><b>financial_flexibility:<\/b> 0=low flexibility, 0.5=medium flexibility, 1=high flexibility.<\/li><li><b>credibility:<\/b> 0=low credibility, 0.5=medium credibility, 1=high credibility.<\/li><li><b>competitiveness:<\/b> 0=low competitiveness, 0.5=medium competitiveness, 1=high competitiveness.<\/li><li><b>operating_risk:<\/b> 0=low risk, 0.5=medium risk, 1=high risk.<\/li><li><b>class:<\/b> bankruptcy, non-bankruptcy (target variable).<\/li><\/ul><p>On the other hand, the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Instances\">instances<\/a> are divided randomly into training, selection, and testing subsets, containing 60%, 20%, and 20% of the instances, respectively.<\/p><p>Our target variable is <b>class<\/b>. We can calculate the data <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributions<\/a> and plot a pie chart with the percentage of instances for each class.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/bankruptcy_pie_chart.webp\" \/><\/p><p>As we can see, the target variable is exceptionally well-balanced. Indeed, 42.8% are positive samples and 57.8% are negative samples.<\/p><p>The inputs-targets correlations might indicate which factors have the greatest influence on going into bankruptcy.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/bankruptcy_correlations.webp\" \/><\/p><p>In this example, the variables that correlate the most with the target variable negatively correlate.\u00a0<i>Competitiveness<\/i> is the variable with the highest correlation.<\/p><\/section><section><h3>3. Neural network<\/h3><p>The next step is to set the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">neural network<\/a> parameters. For classification problems, it is composed of:<\/p><ul><li>Scaling layer.<\/li><li>Perceptron layers.<\/li><li>Probabilistic layer.<\/li><\/ul><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#MeanStandardDeviationScalingMethod\">mean and standard deviation scaling method<\/a> has been set for the scaling layer.<\/p><p>We set one <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#PerceptronsLayers\">perceptron layer<\/a> with 3 neurons with the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#LogisticActivationFunction\">logistic activation function<\/a>. Since the target variable is binary, this layer has six inputs and only one output.<\/p><p>The neural network for this example can be represented with the following diagram:<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/bankruptcy_nn.webp\" \/><\/p><\/section><section><h2>4. Training strategy<\/h2><p>The fourth step is to set the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\">training strategy<\/a> defining\u00a0what the neural network will learn. A general training strategy for classification is composed of two terms:<\/p><ul><li>A loss index.<\/li><li>An optimization algorithm.<\/li><\/ul><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#LossIndex\">loss index<\/a> chosen for this problem is the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#NormalizedSquaredError\">normalized squared error<\/a> between the outputs from the neural network and the targets in the data set with L1 regularization.<\/p><p>The selected <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#OptimizationAlgorithm\">optimization algorithm<\/a> is the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy#QuasiNewtonMethod\">Quasi-Newton method<\/a>.<\/p><p>The following chart shows how training and selection errors develop with the epochs during training. The final values are <b>training error = 0.0357 NSE<\/b> and <b>selection error = 0.0285 NSE<\/b>.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/bankruptcy_errorchart.webp\" \/><\/p><\/section><section><h2>5. Model selection<\/h2><p>The objective of <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\">model selection<\/a> is to improve the neural network&#8217;s generalization capabilities or, in other words, to reduce the selection error.<\/p><p>Since the selection error we have achieved is minimal (0.0285 NSE), there is no need to apply an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#OrderSelection\">order selection<\/a> or an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection#InputSelection\">input selection<\/a> algorithm.<\/p><\/section><section><h2>6. Testing analysis<\/h2><p>The objective of the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing analysis<\/a> is to validate the generalization performance of the trained neural network.<br \/>To validate a classification technique, we need to compare the values provided by this technique to the observed values. We can use the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#RocCurve\">ROC curve<\/a>\u00a0as it is the standard testing method for binary classification projects.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/bankruptcy_ROC.webp\" \/><\/p><p>The AUC value for this example is 1.<\/p><p>The following table contains the elements of the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#ConfusionMatrix\">confusion matrix<\/a>. This matrix contains the variable class&#8217;s true positives, false positives, false negatives, and true negatives.<\/p><div style=\"overflow-x: auto;\"><table><tbody><tr><th>\u00a0<\/th><th>Predicted positive<\/th><th>Predicted negative<\/th><\/tr><tr><th>Real positive<\/th><td style=\"text-align: right;\">20 (40%)<\/td><td style=\"text-align: right;\">0 (0%)<\/td><\/tr><tr><th>Real negative<\/th><td style=\"text-align: right;\">0 (0%)<\/td><td style=\"text-align: right;\">30 (60%)<\/td><\/tr><\/tbody><\/table><\/div><p>The total number of testing samples is 50, all correctly classified.<\/p><p>We can also perform the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#CumulativeGain\">cumulative gain<\/a> analysis, a visual aid that shows the advantage of using a predictive model instead of randomness.<\/p><p>It consists of three lines. The baseline represents the results that would be obtained without using a model. The positive cumulative gain shows in the y-axis the percentage of positive instances found against the population represented in the x-axis.<br \/>Similarly, the negative cumulative gain shows the percentage of the negative instances found against the population percentage.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/bankruptcy_cumulative_gain.webp\" \/><\/p><p>In this case, by using the model, we see that by analyzing 40% of the businesses with a higher probability of going bankrupt, we would reach 100% of the companies that will go bankrupt.<\/p><\/section><section><h2>7. Model deployment<\/h2><p>In the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\">model deployment<\/a> phase, we use the neural network to predict the risk of a company.<\/p><h3>Mathematical expression<\/h3><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment#MathematicalExpression\">mathematical expression<\/a> represented by the predictive model is listed next:<\/p><pre>scaled_industrial_risk = (industrial_risk-0.5180000067)\/0.4115259945;\nscaled_management_risk = (management_risk-0.6140000224)\/0.4107050002;\nscaled_financial_flexibility = (financial_flexibility-0.3759999871)\/0.4015829861;\nscaled_credibility = (credibility-0.4699999988)\/0.415681988;\nscaled_competitiveness = (competitiveness-0.476000011)\/0.440681994;\nscaled_operating_risk = (operating_risk-0.5699999928)\/0.4345749915;\n            \nperceptron_layer_1_output_00 = logistic( 0.0068644 + (scaled_industrial_risk*0.00202216) + (scaled_management_risk*-0.0209541) + (scaled_financial_flexibility*0.410356) + (scaled_credibility*0.231337) + (scaled_competitiveness*1.40435) + (scaled_operating_risk*-0.00677014) );\nperceptron_layer_1_output_11 = logistic( -0.000410683 + (scaled_industrial_risk*-0.00335452) + (scaled_management_risk*0.00233676) + (scaled_financial_flexibility*0.00435821) + (scaled_credibility*-1.38581e-06) + (scaled_competitiveness*0.00713994) + (scaled_operating_risk*-0.000435457) );\nperceptron_layer_1_output_22 = logistic( 0.198992 + (scaled_industrial_risk*0.00149979) + (scaled_management_risk*-0.0878777) + (scaled_financial_flexibility*0.721492) + (scaled_credibility*0.569166) + (scaled_competitiveness*2.23102) + (scaled_operating_risk*-0.0712571) );\n            \nprobabilistic_layer_combinations_0 = 2.77521 -2.3135*perceptron_layer_1_output_0 +0.00396173*perceptron_layer_1_output_1 -4.69263*perceptron_layer_1_output_2 \n                \nclass = 1.0\/(1.0 + exp(-probabilistic_layer_combinations_0);\n<\/pre><p>This formula can also be exported to the software tool the company requires.<\/p><\/section><section><h2>References<\/h2><ul><li>The data for this problem has been taken from the Machine Learning <a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/Qualitative_Bankruptcy\">UCI Repository<\/a>.<\/li><\/ul><\/section><section><h2>Related posts<\/h2><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":2587,"template":"","categories":[29],"tags":[47],"class_list":["post-3466","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 company bankruptcy through machine learning<\/title>\n<meta name=\"description\" content=\"Use machine learning to build a model to assess company bankruptcy risk from experts&#039; qualitative parameters.\" \/>\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\/bankruptcy-prevention\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bankruptcy prediction machine learning example\" \/>\n<meta property=\"og:description\" content=\"Bankruptcy is a legal proceeding involving a person or business that is unable to repay their outstanding debts. 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