Personal bankruptcy prediction using decision tree model
Keywords:
Data mining, Credit scoring, Decision tree model, Personal bankruptcy, Random undersamplingAbstract
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 131,282 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 and not 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 the decision tree technique.
Design/methodology/approach. In this paper, bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24,546 cases with 17 per cent settled cases and 83 per cent 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.
Social implications. Create awareness to society on significant variables of personal bankruptcy so that they can avoid being a bankrupt.
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.
Downloads
References
Abdou, H. and Pointon, J. (2011), “Credit scoring, statistical techniques and evaluation criteria: a review of the literature”, Intelligent Systems in Accounting, Finance and Management, Vol. 18 Nos 2/3, pp. 59-88, doi: 10.1002/isaf.325.
Agarwal, S., Chomsisengphet, S. and Liu, C. (2011), “Consumer bankruptcy and default: the role of individual social capital”, Journal of Economic Psychology, Vol. 32 No. 4, pp. 632-650, doi: 10.1016/j.joep.2010.11.007.
Akosa, J.S. (2017), “Predictive accuracy: a misleading performance measure for highly imbalanced data classified negative”, SAS Global Forum, pp. 1-12, available at: http://support.sas.com/resources/papers/proceedings17/0942-2017.pdf
Azaizeh, S.Y. (2010), “Essays on household demand for credit cards, bankruptcy and overspending”, Available from ProQuest Dissertations and Theses Global, 763225189, available at: http://search.proquest.com.ezaccess.library.uitm.edu.my/docview/763225189?accountid=42518
Bernama (2016), “Alarming 22,581 M’sians aged 25 to 34 declared bankrupt in last four years”, New Strait Times, available at: www.nst.com.my/news/2016/11/189631/alarming-22581-msians-aged-25-34-declared-bankrupt-last-four-years
Bland, E.M., Christi, C. and Stokes, P.P. (2007), “A comparison of perceptions: students and bankruptcy filers”, Journal of Business and Economics Research, Vol. 5 No. 7, pp. 53-64.
Brown, I. (2014), Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT, SAS Institute, Cary, NC.
Chen, M.Y. (2011), “Predicting corporate financial distress based on integration of decision tree classification and logistic regression”, Expert Systems with Applications, Vol. 38 No. 9, pp. 11261-11272.
Choy, M. and Flom, P. (2010), “Building decision trees from decision stumps”, SAS Global Forum 2010, (094), pp. 1-7.
Daskalaki, S., Kopanas, I., Goudara, M. and Avouris, N. (2003), “Data mining for decision support on customer insolvency in telecommunications business”, European Journal of Operational Research, Vol. 145 No. 2, pp. 239-255, doi: 10.1016/S0377-2217(02)00532-5.
Dawsey, A. (2014), “Externalities among creditors and personal bankruptcy”, Journal of Financial Economic Policy, Vol. 6 No. 1, pp. 2-24, doi: 10.1108/JFEP-09-2013-0037.
Dick, A. and Lehnert, A. (2010), “Personal bankruptcy and credit market competition”, The Journal of Finance, Vol. 65 No. 2, available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2009.01547.x/full
Eaw, H.C., Khong, K.W., Rajagopalan, U. and A. Hamid, B. (2014), “Causality of personal bankruptcy in Malaysia”, Emerging Trends in Scientific Research, April 2006, pp. 363-367.
Eaw, H.C., Rajagopalan, U., Hamid, B.A., Khong, K.W. and Ahmad, R. (2015), “Psychographic factors: does it influence personal bankruptcy in Malaysia?”, Journal of Business and Economic Studies, Vol. 1 No. 2.
Fisher, J.D. (2005), “The effect of unemployment benefits, welfare benefits, and other income on personal bankruptcy”, Contemporary Economic Policy, Vol. 23 No. 4, pp. 483-492, doi: 10.1093/cep/.
Gepp, A. and Kumar, K. (2015), “Predicting financial distress: a comparison of survival analysis and decision tree techniques”, Procedia Computer Science, Vol. 54, pp. 396-404, available at: https://doi.org/10.1016/j.procs.20
Irimia-Dieguez, A.I. and Blanco-Oliver Vazquez-Cueto, M.J. (2015), “A comparison of classification/regression trees and logistic regression in failure models”, Procedia Economics and Finance, Vol. 23, pp. 9-14, available at: https://doi.org/10.1016/S2212-5671(15)00493-1
Jullamon, K. (2012), “Consumer bankruptcy in Thailand”, Available from ProQuest Dissertations and Theses Global, 562209086, available at: http://search.proquest.com.ezaccess.library.uitm.edu.my/docview/1562209086?accountid=42518
Linoff, G.S. and Berry, M.J.A. (2011), Data Mining Techniques: for Marketing, sales, and Customer Relationship Management, 3rd ed., John Wiley and Sons, Hoboken, NJ.
Livshits, I., MacGee, J. and Tertilt, M. (2010), “Accounting for the rise in consumer bankruptcies”, American Economic Journal: Macroeconomics, Vol. 2 No. 2, pp. 165-193, doi: 10.1257/mac.2.2.165.
Malaysia Department of Insolvency (2017), “Annual report”, Bahagian Hal Ehwal Undang-Undang, Jabatan Perdana Menteri, available at: www.bheuu.gov.my/portal/pdf
Min, J.H. and Lee, Y.C. (2008), “A practical approach to credit scoring”, Expert Systems with Applications, Vol. 35 No. 4, pp. 1762-1770, doi: 10.1016/j.eswa.2007.08.070.
Mirzei, M., Ramakrishnan, S. and Bekri, M. (2016), “Corporate default prediction with industry effects: evidence from emerging markets”, International Journal of Economics and Financial Issues, Vol. 6 No. 2001, pp. 161-169.
Nair, Y., Paim, L., Sabri, M.F. and Rahim, H.A. (2016), “Predictors of bankruptcy probability among Malaysian civil servants: examining the subjective measurement”, Journal of Emerging Economies and Islamic Research, Vol. 2011 No. 2011, pp. 1-12.
Noordin, N., Zakaria, Z., Zool, M., Mohamed, H. and Ngah, K. (2012), “Bankruptcy among young executives in Malaysia”, International Conference on Economics, Marketing and Management, Vol. 28, pp. 132-136.
Olson, D.L., Delen, D. and Meng, Y. (2012), “Comparative analysis of data mining methods for bankruptcy prediction”, Decision Support Systems, Vol. 52 No. 2, pp. 464-473, doi: 10.1016/j.dss.2011.10.007.
Othman, M.A., Abdul Rahim, H. and Sabri, M.F. (2015), “Differences in financial information and proceedings of the Australasian conference on business and social sciences 2015”, pp. 525-531, available at: www.aabss.org.au/system/files/published/000959-published-acbss-2015-sydney.pdf
Paleologo, G., Elisseeff, A. and Antonini, G. (2010), “Subagging for credit scoring models”, European Journal of Operational Research, Vol. 201 No. 2, pp. 490-499, doi: 10.1016/j.ejor.2009.03.008.
Řezáč, M. and Řezáč, F. (2011), “How to measure the quality of credit scoring models. Finance a uver”, Czech Journal of Economics and Finance, Vol. 61 No. 5, pp. 486-507.
Sarma, K.S. (2017), Predictive Modeling with SAS Enterprise Miner: practical Solutions for Business Applications, SAS Institute, Cary, NC.
Selvanathan, M., Krisnan, U.D. and Wen, W.C. (2016), “Factors effecting towards personal bankruptcy among residents: case study in Klang valley, Malaysia”, International Journal of Human Resource Studies, Vol. 6 No. 3, p. 98, doi: 10.5296/ijhrs.v6i3.10092.
Tsai, C.F. (2014), “Combining cluster analysis with classifier ensembles to predict financial distress”, Information Fusion, Vol. 16 No. 1, pp. 46-58, doi: 10.1016/j.inffus.2011.12.001.
Ville, B.D. (2006), “Decision trees – what are they?”, Decision Trees for Business Intelligence and Data Mining, SAS Institute, Cary, NC, pp. 1-4, available at: http://support.sas.com/publishing/pubcat/chaps/57587.pdf
Xiong, T., Wang, S., Mayers, A. and Mong, E. (2013), “Personal bankruptcy prediction by mining credit card data”, Expert Systems with Applications, Vol. 40 No. 2, pp. 665-676, doi: 10.1016/j.eswa.2012.07.072.
Yap, B.W., Rahman, H.A.A., He, H. and Bulgiba, A. (2016), “Handling imbalanced dataset using SVM and k-NN approach”, in AIP Conference Proceedings, AIP Publishing, Vol. 1750 No. 1, p. 20023.
Yap, B.W., Ong, S.H. and Husain, N.H.M. (2011), “Using data mining to improve assessment of credit worthiness via credit scoring models”, Expert Systems with Applications, Vol. 38 No. 10, pp. 13274-13283, available at: https://doi.org/10.1016/j.eswa.2011.04.147
Zainon, Z. (2016), “AKPK selesai masalah kewangan RM476.6j: Utusan online”, available at: www.utusan.com.my/bisnes/korporat/akpk-selesai-masalah-kewangan-rm476-6j-1.417133#nav-allsections
Zhu, N. (2013), “Household consumption and personal bankruptcy”, Journal of Legal Studies, Vol. 40 No. 1, pp. 1-37.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Journal of Economics, Finance and Administrative Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
