Personal bankruptcy prediction using decision tree model

• Autores: Syed Nor, Sharifah Heryati, Ismail, Shafinar, Yap, Bee Wah
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Universidad ESAN
• Repositorio: ESAN-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.esan.edu.pe:20.500.12640/1909
• Enlace del recurso: https://revistas.esan.edu.pe/index.php/jefas/article/view/88; https://hdl.handle.net/20.500.12640/1909; https://doi.org/10.1108/JEFAS-08-2018-0076
• Nivel de acceso: acceso abierto
• Materia: Data mining; Credit scoring; Decision tree model; Personal bankruptcy; Random undersampling; Quiebra personal; Minería de datos; Árbol de decisiones; Puntuación de crédito; Submuestreo aleatorio; https://purl.org/pe-repo/ocde/ford#5.02.04

Purpose – Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007 and the total accumulated personal bankruptcy cases stood at 131282 in 2014. This is indeed an alarming Issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy as well as on the society. From the aspect of individual’s personal economy bankruptcy minimizes their chances of securing a job. Apart from that their account will be frozen lost control on their assets and properties andnot allowed to start any business nor be a part of any company’s management. Bankrupts also will be denied from any loan application restricted from travelling overseas and cannot act as a guarantor. This paper aims to investigate this problem by developing the personal bankruptcy prediction model using thedecision tree technique. Design/methodology/approach – In this paper bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24546 cases with 17 per cent settled cases and 83 percent terminated cases. The data included a dependent variable i.e. bankruptcy status (Y = 1(bankrupt)Y = 0 (non-bankrupt)) and 12 predictors. SAS Enterprise Miner 14.1 software was used to develop the decision tree model. Findings – Upon completion this study succeeds to come out with the profiles of bankrupts reliable personal bankruptcy scoring model and significant variables of personal bankruptcy. Practical implications – This decision tree model is possible for patent and income generation. Financial institutions are able to use this model for potential borrowers to predict their tendency toward personal bankruptcy. Originality/value – This decision tree model is able to facilitate and assist financial institutions in evaluating and assessing their potential borrower. It helps to identify potential defaulting borrowers. It also can assist financial institutions in implementing the right strategies to avoid defaulting borrowers.