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A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation
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For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower鈥檚 characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVM-LSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management." /> <meta name="title" content="A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation" /> <meta name="googlebot" content="NOODP" /> <meta name="citation_title" content="A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation" /> <meta name="citation_author" content="HASNI, Marwa" /> <meta name="citation_author_institution" content="Optimisation et Analyse des Syst&egrave;mes Industriels et de Service Ecole Nationale d&rsquo;Ing&eacute;nieurs de Tunis Laboratoire G&eacute;nie Industriel" /> <meta name="citation_author" content="AGUIR, Mohamed Salah" /> <meta name="citation_author_institution" content="Ecole Nationale d&#039;Ing&eacute;nieurs de Carthage" /> <meta name="citation_author" content="Babai, Mohamed Zied" /> <meta name="citation_author_institution" content="kedge business school" /> <meta name="citation_author" content="JEMAI, Zied" /> <meta name="citation_author_institution" content="Ecole Nationale d&rsquo;Ing&eacute;nieurs de Tunis Laboratoire G&eacute;nie Industriel Centrale Supelec, University of Paris Saclay Paris, France" /> <meta name="citation_abstract" content="Credit risk consists is the expectation of losses stemming from the inability of a borrower to repay a loan. For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower鈥檚 characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVM-LSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management." /> <meta name="citation_id" content="2928" /> <meta name="citation_publication_date" content="2024/05/01" /> <meta name="citation_date" content="2024-05-01" /> <meta name="citation_journal_title" content="International Journal of Supply and Operations Management" /> <meta name="citation_issn" content="23831359" /> <meta name="citation_volume" content="11" /> <meta name="citation_issue" content="2" /> <meta name="citation_firstpage" content="168" /> <meta name="citation_lastpage" content="187" /> <meta name="citation_publisher" content="Kharazmi University" /> <meta name="citation_doi" content="10.22034/ijsom.2023.109898.2722" /> <meta name="DC.Identifier" content="10.22034/ijsom.2023.109898.2722" /> <meta name="citation_abstract_html_url" content="http://www.ijsom.com/article_2928.html" /> <meta name="citation_pdf_url" content="http://www.ijsom.com/article_2928_98299b6957904ad25f194b788bc4138f.pdf" /> <meta name="DC.Title" content="A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation" /> <meta name="DC.Source" content="International Journal of Supply and Operations Management" /> <meta name="DC.Date" content="01/05/2024" /> <meta name="DC.Date.issued" content="2024-05-01" /> <meta name="DC.Format" content="application/pdf" /> <meta name="DC.Contributor" content="HASNI, Marwa" /> <meta name="DC.Contributor" content="AGUIR, Mohamed Salah" /> <meta name="DC.Contributor" content="Babai, Mohamed Zied" /> <meta name="DC.Contributor" content="JEMAI, Zied" /> <meta name="og:title" content="A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation" /> <meta name="og:description" content="Credit risk consists is the expectation of losses stemming from the inability of a borrower to repay a loan. For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower鈥檚 characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVM-LSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management." /> <meta name="og:url" content="http://www.ijsom.com/article_2928.html" /> <!-- WEB FONTS : use %7C instead of | (pipe) --> <link href="./themes/base/front/assets/css/social-icon-font.css" rel="stylesheet" type="text/css" /> <!-- CORE CSS --> <link href="./themes/base/front/assets/plugins/bootstrap/css/bootstrap.min.css?v=0.02" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/header.css?v=0.05" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/footer.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/essentials.css?v=0.2" rel="stylesheet" type="text/css" /> <link href="./inc/css/cookieconsent.min.css" rel="stylesheet" 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<section class="no-cover-box"> <div class="row"> <!-- CENTER --> <div class="col-lg-9 col-md-9 col-sm-8" id="dv_artcl"> <!-- Current Issue --> <div> <h1 class="margin-bottom-20 size-18 ltr"><span class="article_title bold"> <a href="./article_2928_98299b6957904ad25f194b788bc4138f.pdf" target="_blank">A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation</a></span></h1> <div> <div class="margin-bottom-3"> </div> <p class="margin-bottom-3">Document Type : TORS 2022</p> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Authors</strong></p> <ul class="list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a href="./?_action=article&au=130057&_au=Marwa++HASNI">Marwa HASNI</a> <sup><a href="mailto:gmarwa92@gmail.com" data-toggle="tooltip" data-placement="bottom" title="Email to Corresponding Author"><i class="fa fa-envelope-o" ></i></a></sup> <sup class="ltr"><a class=" text-green" href="https://www.orcid.org/0000-0002-4509-5563" data-toggle="tooltip" data-placement="bottom" data-html="true" title="ORCID: 0000-0002-4509-5563" target="_blank"><i class="ai ai-orcid size-13" ></i></a></sup> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=130600&_au=Mohamed+Salah++AGUIR">Mohamed Salah AGUIR</a> <sup><a href="#aff2" >2</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=130374&_au=Mohamed+Zied++Babai">Mohamed Zied Babai</a> <sup class="ltr"><a class=" text-green" href="https://www.orcid.org/0000-0001-7107-6924" data-toggle="tooltip" data-placement="bottom" data-html="true" title="ORCID: 0000-0001-7107-6924" target="_blank"><i class="ai ai-orcid size-13" ></i></a></sup> <sup><a href="#aff3" >3</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=130601&_au=Zied++JEMAI">Zied JEMAI</a> <sup><a href="#aff4" >4</a></sup> </li> </ul> <p class="margin-bottom-3 ltr" id="aff1"> <sup>1</sup> Optimisation et Analyse des Syst&egrave;mes Industriels et de Service Ecole Nationale d&rsquo;Ing&eacute;nieurs de Tunis Laboratoire G&eacute;nie Industriel </p> <p class="margin-bottom-3 ltr" id="aff2"> <sup>2</sup> Ecole Nationale d&#039;Ing&eacute;nieurs de Carthage </p> <p class="margin-bottom-3 ltr" id="aff3"> <sup>3</sup> kedge business school </p> <p class="margin-bottom-3 ltr" id="aff4"> <sup>4</sup> Ecole Nationale d&rsquo;Ing&eacute;nieurs de Tunis Laboratoire G&eacute;nie Industriel Centrale Supelec, University of Paris Saclay Paris, France </p> <div class="margin-bottom-3 ltr" id="ar_doi" title="DOI"><i class="ai ai-doi size-25 text-orange"></i> <span dir="ltr"><a href="https://dx.doi.org/10.22034/ijsom.2023.109898.2722">10.22034/ijsom.2023.109898.2722</a></span></div> <p style="margin:12px -2px 0 -2px"><strong>Abstract</strong></p> <div class="padding_abstract justify ltr">Credit risk consists is the expectation of losses stemming from the inability of a borrower to repay a loan. For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower’s characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVM-LSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management.</div> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Keywords</strong></p> <ul class="block list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=1857&_kw=Forecasting" >Forecasting</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106485&_kw=Credit+Risk+Management" >Credit Risk Management</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106486&_kw=Deep+Learning" >Deep Learning</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106487&_kw=LSTM" >LSTM</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106488&_kw=SVM" >SVM</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=105076&_kw=Time+Series" >Time Series</a> </li> </ul> </div> <hr> <div class="page_break"></div> <div class="panel"> <div class="panel-heading card-header"> <h4 class="panel-title "> <a data-toggle="collapse" data-parent="#accordions" href="#collapsesRef"><i class="fa fa-plus"></i> References</a> </h4> </div> <div id="collapsesRef" class="panel-collapse collapse"> <div class="panel-body justify"> <div class="padding-3 margin-top-3 ltr justify">Abdesslem, R. 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class="panel-collapse collapse in"> <div class="panel-body ar_info_pnl"> <ul class="list-group list-group-bordered list-group-noicon" style="display:block !important;max-height:9999px"> <li class="list-group-item"><a class="tag_a">Article View: <i>727</i></a></li> <li class="list-group-item"><a class="tag_a">PDF Download: <i>603</i></a></li> </ul> </div> </div> </div> </div> <!-- /LEFT --> </div> </section> <div id="cite-apa" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">APA</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>HASNI, M., AGUIR, M., Babai, M., & JEMAI, Z. (2024). A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation. <em>International Journal of Supply and Operations Management</em>, 11(2), 168-187. doi: 10.22034/ijsom.2023.109898.2722</p> </div> </div> </div> </div> <div id="cite-mla" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">MLA</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>Marwa HASNI; Mohamed Salah AGUIR; Mohamed Zied Babai; Zied JEMAI. "A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation". <em>International Journal of Supply and Operations Management</em>, 11, 2, 2024, 168-187. doi: 10.22034/ijsom.2023.109898.2722</p> </div> </div> </div> </div> <div id="cite-harvard" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">HARVARD</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>HASNI, M., AGUIR, M., Babai, M., JEMAI, Z. (2024). 'A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation', <em>International Journal of Supply and Operations Management</em>, 11(2), pp. 168-187. doi: 10.22034/ijsom.2023.109898.2722</p> </div> </div> </div> </div> <div id="cite-vancouver" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">VANCOUVER</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>HASNI, M., AGUIR, M., Babai, M., JEMAI, Z. A Machine Learning Model for Accurate Credit Risk Forecasting in Banking Systems: An Empirical Investigation. <em>International Journal of Supply and Operations Management</em>, 2024; 11(2): 168-187. doi: 10.22034/ijsom.2023.109898.2722</p> </div> </div> </div> </div> </div> </div> <!-- /MAIN CONTENT --> <!-- Subscribe --> <section class="alternate padding-xxs"> </section> <!-- /Subscribe --> <!-- FOOTER --> <div class="container"> <footer id="footer"> <div class="scrollup" id="scroll" href="#"><span></span></div> <div class="row"> <div class="col-md-2"> <!-- Links --> <h4 class="">Explore Journal</h4> <ul class="footer-links list-unstyled"> <li id="fli_home"><a href="./">Home</a></li> <li id="fli_about"><a href="./journal/about">About Journal</a></li> <li id="fli_Edb"><a href="./journal/editorial.board">Editorial Board</a></li> <li id="fli_submit"><a href="./author">Submit Manuscript</a></li> <li id="fli_contactus"><a href="./journal/contact.us">Contact Us</a></li> <li id="fli_glossary"><a href="./journal/glossary">Glossary</a></li> <li id="fli_order_hrdj"><a href="./journal/subscription.form">Hard Copy Subscription</a></li> <li id="fli_sitemap"><a href="./sitemap.xml?usr">Sitemap</a></li> </ul> <!-- /Links --> </div> <div class="col-md-3"> <!-- Latest News --> <h4 class="">Latest News</h4> <ul class="footer-posts list-unstyled"> <li> <a href="./news?newsCode=173">SD of ISC: Sustainable Development of Intelligent Supply Chains based on Trends and Future Directions: Application of Novel Solution Techniques</a> <small class="ltr">2023-03-05</small> </li> </ul> <!-- /Latest News --> </div> <div class="col-md-3"> <!-- Footer Note --> <div><p><a title="Linkedin" href="http://www.linkedin.com/company/ijsom?trk=eml-cp_mktg-btn-welcome-20120607%2F"><img src="images/linkedin.jpg" alt="linkedin" /></a></p></div> <!-- /Footer Note --> </div> <div class="col-md-4"> <!-- Newsletter Form --> <h4 class="">Newsletter Subscription</h4> <p>Subscribe to the journal newsletter and receive the latest news and updates</p> <form class="validate" action="" method="post" data-success="Subscription saved successfully." data-toastr-position="bottom-right"> <input type="hidden" name="_token" value="026269544207c49ef4c202836aac2514a6959a87f76a9ffc"/> <div class="input-group"> <span class="input-group-addon"><i class="fa fa-envelope"></i></span> <input type="email" id="email" name="email" required="required" class="form-control required sbs_email" placeholder="Enter your Email" oninvalid="this.setCustomValidity('Enter a valid email address.')" oninput="this.setCustomValidity('')"> <span class="input-group-btn"> <button class="btn btn-primary mybtn" type="submit">Subscribe</button> </span> </div> </form> <!-- /Newsletter Form --> <!-- Social Icons --> <div class="margin-top-20"> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Facebook"> <i class="fa fa-facebook-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Twitter"> <i class="fa fa-twitter-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Linkedin"> <i class="fa fa-linkedin-square" aria-hidden="true"></i> </a> <a class="noborder" href="./ju.rss" class="social-icon social-icon-border social-rss pull-left block" data-toggle="tooltip" data-placement="top" title="Rss"><i class="fa fa-rss-square" aria-hidden="true"></i></a> </div> </div> </div> <div class="copyright" style="position: relative"> <ul class="nomargin list-inline mobile-block"> <li>© Journal Management System. <span id='sp_crt'>Powered by <a target='_blank' href='https://www.sinaweb.net/'>Sinaweb</a></span></li> </ul> </div> </footer> </div> <!-- /FOOTER --> </div> <!-- /wrapper --> <!-- SCROLL TO TOP --> <a href="#" id="toTop_old"></a> <!-- PRELOADER --> <div id="preloader"> <div class="inner"> <span class="loader"></span> </div> </div><!-- /PRELOADER --> <!-- JAVASCRIPT FILES --> <script type="text/javascript">var plugin_path = './themes/base/front/assets/plugins/';</script> <script type="text/javascript" src="./themes/base/front/assets/js/scripts.js?v=0.02"></script> <!-- user defined scripts--> <!-- Extra Script Scripts --> <script type="text/javascript"> $('ul.nav li.dropdown').hover(function() { if (window.matchMedia('(max-width: 767px)').matches) return; 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