Personal Bankruptcy Prediction Using Decision Tree Model

14 Pages Posted: 7 Jun 2019

See all articles by Sharifah Syed Nor

Sharifah Syed Nor

Universiti Teknologi MARA (UiTM) - Department of Economics and Financial Studies

Shafinar Ismail

Universiti Teknologi MARA (UiTM) - Faculty of Business and Management

Bee Wah Yap

Universiti Teknologi MARA (UiTM)

Date Written: June 5, 2019

Abstract

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.

Keywords: Data mining, Credit scoring, Decision tree model, Personal bankruptcy, Random undersampling

Suggested Citation

Syed Nor, Sharifah and Ismail, Shafinar and Yap, Bee Wah, Personal Bankruptcy Prediction Using Decision Tree Model (June 5, 2019). Journal of Economics, Finance & Administrative Science, Vol. 24, No. 47, 2019, Available at SSRN: https://ssrn.com/abstract=3399733

Sharifah Syed Nor (Contact Author)

Universiti Teknologi MARA (UiTM) - Department of Economics and Financial Studies ( email )

Malaysia

Shafinar Ismail

Universiti Teknologi MARA (UiTM) - Faculty of Business and Management ( email )

40450 Shah Alam
Selangor
Malaysia

Bee Wah Yap

Universiti Teknologi MARA (UiTM)

40450 Shah Alam
Johor
Dungun, Selangor 23000
Malaysia

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