Predicting Bankruptcy Using Machine Learning Algorithms

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Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies."/> <meta name="keywords" content="Machine Learning, Classification, Regression, Correlation, Error Matrix, ROC"/>   <meta name="dc.title" content="Predicting Bankruptcy Using Machine Learning Algorithms"> <meta name="citation_author" content="Abhishek Karan"> <meta name="citation_author" content="Preetham Kumar"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal on Cybernetics & Informatics (IJCI),Vol. 5, No. 1"> <meta name="dc.date" content="2016/2/28"> <meta name="dc.identifier" content="10.5121/ijci.2016.5110"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies."/> <meta name="dc.subject" content="Machine Learning"> <meta name="dc.subject" content="Classification"> <meta name="dc.subject" content="Correlation"> <meta name="dc.subject" content="Error Matrix"> <meta name="dc.subject" content="ROC">   <meta name="prism.publicationName" content="International Journal on Cybernetics & Informatics (IJCI),Vol. 5, No. 1"> <meta name="prism.publicationDate" content="2016/2/28"> <meta name="prism.volume" content="5"> <meta name="prism.number" content="1"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="91">   <meta name="citation_journal_title" content="International Journal on Cybernetics & Informatics (IJCI),Vol. 5, No. 1"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_author" content="Abhishek Karan"> <meta name="citation_author" content="Preetham Kumar"> <meta name="citation_title" content="Predicting Bankruptcy Using Machine Learning Algorithms"> <meta name="citation_online_date" content="2016/2/28"> <meta name="citation_volume" content="5"> <meta name="citation_issue" content="1"> <meta name="citation_firstpage" content="91"> <meta name="citation_author" content="Abhishek Karan"> <meta name="citation_author" content="Preetham Kumar"> <meta name="citation_doi" content="10.5121/ijci.2016.5110"> <meta name="citation_abstract_html_url" content="https://ijcionline.com/abstract/5116ijci10"> <meta name="citation_pdf_url" content="https://aircconline.com/ijci/V5N1/5116ijci10.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://ijcionline.com/abstract/5116ijci10"/> <meta property="og:title" content="Predicting Bankruptcy Using Machine Learning Algorithms"> <meta property="og:description" content="This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. 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Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies </p> </div> </div> <div class="card"> <h5 id="about" class="brown-text text-darken-2 text-center" style="padding-bottom:0px">Keywords</h5>  <div class="card-content"> <p class="left-text" style="text-align:justify"> Machine Learning, Classification, Regression, Correlation, Error Matrix, ROC</p></div> </div> <div class="card-content"> <a href="https://aircconline.com/ijci/V5N1/5116ijci10.pdf" target="blank" class="btn btn-small lighten-2 cyan lig">Full Text</a>  <a href="https://ijcionline.com/vol5" class="btn btn-small lighten-2 cyan lig">Volume 5</a> </div> </div>  <div id="side-bar" class="col s12 m3"> <div id="section-main"> <br> <br> <div class="card side cyan lighten-2"> <div class="card-content"> <ul> <li class="ax waves-effect waves-light"> <a class="white-text" href="/editorial" > <i class="material-icons left">account_circle</i>Editorial Board</a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/mostcitedarticels" > <i class="material-icons left">book</i>Most Cited Articels </a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/indexing" > <i class="material-icons left">list</i>Indexing </a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/faq" > <i class="material-icons left">help</i>FAQ </a> <br> </li> <br> <br> <div class="divider"></div> <br> </div> </div> </div> </div> </div> </div> </div> </section> <br> <br> <br> <br> <div id="txtcnt"></div>  <footer class="page-footer cyan lighten-3"> <div class="nav-wrapper"> <div class="container"> <ul> <li> <a target="_blank" href="http://airccse.org/"> <img src="/img/since2008.png" alt="since2008"></a> </li> </ul> <h6> Free Open Access Conference Proceedings <br> Computer Science & Engineering - Information Technology - Information Systems</h6> </div> <div class="footer-m col m3 s12 offset-m1"> </div> <div class="social col m3 offset-m1 s12"> </div> </div> </div> <div class="col s12 m10 offset-m1"> <div class="grey darken-3 center-align"> <small class="white-text">Designed and Developed by NNN Team</small> </div> </div> </footer> </body>  <script type="text/javascript" src="https://code.jquery.com/jquery-3.2.1.min.js"></script> <script type="text/javascript" src="/js/materialize.min.js"></script> <script src="/js/search.js"></script> <script src="/js/scrolltop.js"></script> <script src="/js/popup.js"></script> <script src="/js/main.jquery.js"></script> </html>

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Predicting Bankruptcy Using Machine Learning Algorithms