Advanced Machine Learning Algorithm for Credit Card Fraud Detection

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Algorithm for Credit Card Fraud Detection</title> <meta name="description" content="Advanced Machine Learning Algorithm for Credit Card Fraud Detection"> <meta name="keywords" content="automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img 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publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Advanced Machine Learning Algorithm for Credit Card Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manpreet%20Kaur">Manpreet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours. <iframe src="https://publications.waset.org/abstracts/167417.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20fraud%20detection" title="automated fraud detection">automated fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=isolation%20forest%20method" title=" isolation forest method"> isolation forest method</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20outlier%20factor" title=" local outlier factor"> local outlier factor</a>, <a href="https://publications.waset.org/abstracts/search?q=ML%20algorithm" title=" ML algorithm"> ML algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20card" title=" credit card"> credit card</a> </p> <a href="https://publications.waset.org/abstracts/167417/advanced-machine-learning-algorithm-for-credit-card-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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Advanced Machine Learning Algorithm for Credit Card Fraud Detection