{"id":3574,"date":"2025-11-25T11:13:19","date_gmt":"2025-11-25T10:13:19","guid":{"rendered":"https:\/\/neuraldesigner.com\/solution\/fraud-detection\/"},"modified":"2026-02-09T11:51:36","modified_gmt":"2026-02-09T10:51:36","slug":"fraud-detection","status":"publish","type":"solution","link":"https:\/\/www.neuraldesigner.com\/solutions\/fraud-detection\/","title":{"rendered":"Fraud detection using machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3574\" class=\"elementor elementor-3574\" data-elementor-post-type=\"solution\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-67215c5c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"67215c5c\" 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-207a37bf\" data-id=\"207a37bf\" 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-14fdd384 elementor-widget elementor-widget-text-editor\" data-id=\"14fdd384\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><\/p>\n<section>\n<p>Analyzing a company&#8217;s payments or transaction data allows us to classify which ones are fraudulent and not.<\/p>\n<p>Knowing this classification, we can prevent them in our company.<\/p>\n<p>The data science and machine learning platform&nbsp;<a href=\"https:\/\/www.neuraldesigner.com\/solutions\/fraud-detection\">Neural Designer<\/a>&nbsp;helps banks, insurance, telecommunications, and retail companies to detect and avoid these fraudulent payments.<\/p>\n<h3>Contents<\/h3>\n<ol>\n<li><a href=\"#Objectives\">Objectives<\/a>.<\/li>\n<li><a href=\"#Benefits\">Benefits<\/a>.<\/li>\n<li><a href=\"#Approach\">Approach<\/a>.<\/li>\n<li><a href=\"#Results\">Results<\/a>.<\/li>\n<li><a href=\"#Conclusions\">Conclusions<\/a>.<\/li>\n<\/ol>\n<\/section>\n<p><!-- Definition --><\/p>\n<section id=\"Objectives\">\n<h2>Objectives<\/h2>\n<p>Fraud detection has become one of the biggest challenges for all companies. Frauds represent significant problems for any business, and specialized analysis techniques for detecting them are required.<\/p>\n<p>However, it is not easy to develop these techniques to detect fraud.<\/p>\n<p>By harnessing the power of fraud data sets, companies can develop predictive models that enable the detection of any possible fraud.<\/p>\n<p><img decoding=\"async\" class=\"\" style=\"width: 484px; max-width: 100%;\" src=\"https:\/\/www.neuraldesigner.com\/images\/fraud-figure.svg\" height=\"253\"><\/p>\n<p>Some of the factors that can influence committing fraud are the following:<\/p>\n<ul>\n<li><b>Buyer variables<\/b>: Gender, age, education level, job category, nationality&#8230;<\/li>\n<li><b>Payment variables<\/b>: Amount, country of origin, decline&#8230;<\/li>\n<li><b>Payment channel<\/b>: Credit card, transfer, mobile phone, check&#8230;<\/li>\n<li><b>Stationary variables<\/b>: Season, date, time&#8230;<\/li>\n<li>Etc.<\/li>\n<\/ul>\n<p>The objective is to develop a program to detect and prevent future fraud.<\/p>\n<p>To do that, it is essential to develop new methods that can identify which are the variables that make a payment fraudulent.<\/p>\n<\/section>\n<p><!-- Benefits --><\/p>\n<section id=\"Benefits\">\n<h2>Benefits<\/h2>\n<p>The following are some of the many benefits of using machine learning for fraud detection.<\/p>\n<p><img decoding=\"async\" class=\"small_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/Warning.svg\" width=\"70\" height=\"70\"><\/p>\n<h3>STOP FRAUD QUICKLY<\/h3>\n<p>Detect fraud quickly and early enough to take action.<\/p>\n<p><img decoding=\"async\" class=\"small_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/trending_up.svg\" width=\"70\" height=\"70\"><\/p>\n<h3>AVOID ECONOMIC LOSSES<\/h3>\n<p>Avoid losses due to fraudulent payments.<\/p>\n<p><img decoding=\"async\" class=\"small_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/search.svg\" width=\"71\" height=\"71\"><\/p>\n<h3>IDENTIFY COMPANY WEAKNESS<\/h3>\n<p>Identify the weakness of why a fraud could be committed and fix them.<\/p>\n<\/section>\n<p><!-- Approach --><\/p>\n<section id=\"Approach\">\n<h2>Approach<\/h2>\n<p><!--<\/p>\n<p>\n        The following flow chart shows how to build and use a fraud-detection model.\n        <\/p>\n<p>\n        <img decoding=\"async\" class=\"tiny_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/number_1.svg\" \/><br \/>        The first step is to create a<br \/>        <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">data set<\/a><br \/>        by collecting all the internal and external information related to different fraudulent and non-fraudulent payments or transactions.\n        <\/p>\n<p>        <img decoding=\"async\" class=\"tiny_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/number_2.svg\" \/><\/p>\n<p>\n        Then, we need to build the<br \/>        <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\">neural network<\/a><br \/>        that will predict which ones are fraudulent.\n        <\/p>\n<p>        <img decoding=\"async\" class=\"tiny_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/number_3.svg\" \/><\/p>\n<p>\n        A <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/training-strategy\">training strategy<\/a><br \/>        is applied to the neural network to discover the underlying relationships in the data.\n        <\/p>\n<p>        <img decoding=\"async\" class=\"tiny_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/number_4.svg\" \/><\/p>\n<p>\n        To improve the predictive capabilities of the model,<br \/>        we can also apply <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-selection\">model selection<\/a> by trying combinations of variables and choosing those with more impact.\n        <\/p>\n<p>        <img decoding=\"async\" class=\"tiny_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/number_5.svg\" \/><\/p>\n<p>\n        Then, the resulting model undergoes an exhaustive<br \/>        <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing analysis<\/a>.\n        <\/p>\n<p>        <img decoding=\"async\" class=\"tiny_left\" src=\"https:\/\/www.neuraldesigner.com\/images\/number_6.svg\" \/><\/p>\n<p>\n        Finally, after <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\">model deployment<\/a>,<br \/>        the neural network is used to predict which payments or transactions are fraudulent.\n        <\/p>\n<p> --><\/p>\n<p>Neural networks can detect fraudulent transactions so that we can avoid them.<\/p>\n<p>The following graph illustrates an example of a neural network for fraud detection:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/fraud-nn.webp\"><\/p>\n<p>The data science and machine learning platform <a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">Neural Designer<\/a>&nbsp;guides you through this process so that you can focus on your business and not on the details behind machine learning.<\/p>\n<\/section>\n<p><!-- Results --><\/p>\n<section id=\"Results\">\n<h2>Results<\/h2>\n<p>As we have explained before, fraud detection aims to determine which payments are fraudulent and which ones are not.<\/p>\n<p>The next graph is obtained from the example <a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/credit-card-fraud\">credit card fraud detection<\/a>. We can observe the rates with the model and without the model.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/vehicle_insurance_prates.webp\"><\/p>\n<p>As expected, we can see how the positive rate increase, initially we have a rate of 15%, and after applying the model, we have a rate of 90%.<\/p>\n<\/section>\n<p><!-- Conclusions --><\/p>\n<section id=\"Conclusions\">\n<h2>Conclusions<\/h2>\n<p>Machine learning allows companies to find frauds on time using the available data.<\/p>\n<p>Companies can take advantage of these techniques and avoid losses due to fraudulent payments. They can also improve all the company&#8217;s weaknesses in the payment area.<\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> uses artificial intelligence to discover the variables that influence whether or not a payment is fraudulent.<\/p>\n<\/section>\n<section>\n<h2>Related posts<\/h2><ul>\n<\/ul>\n<\/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":2148,"template":"","categories":[],"tags":[47],"class_list":["post-3574","solution","type-solution","status-publish","has-post-thumbnail","hentry","tag-finance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Fraud detection using machine learning<\/title>\n<meta name=\"description\" content=\"Discover how to use machine learning to prevent fraud in your company, by analyzing payments or 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