Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin

This paper describes the development of a debt risk predictive model for individual taxpayer in Inland Revenue Board Malaysia (IRBM). Using data mining to predict taxpayer's compliance and non-compliance has gained attention in recent times. However, very little research has been done to predic...

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Main Author:	Mohamed Azidin, Hamisah
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Credit Debt Loans Data mining
Online Access:	https://ir.uitm.edu.my/id/eprint/63443/1/63443.pdf
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id	my-uitm-ir.63443
record_format	uketd_dc
spelling	my-uitm-ir.634432022-07-27T07:18:34Z Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin 2015-07 Mohamed Azidin, Hamisah Credit. Debt. Loans Data mining This paper describes the development of a debt risk predictive model for individual taxpayer in Inland Revenue Board Malaysia (IRBM). Using data mining to predict taxpayer's compliance and non-compliance has gained attention in recent times. However, very little research has been done to predict taxpayers who have debt with tax organizations around the world. The objective of this study is to choose a suitable data mining methodology or framework and what is suitable data mining technique to build a predictive model debt risk for taxpayers who have debts with IRBM. This study also to get the behavior or pattern of data and identify important variables in predicting taxpayer with debt risk. Data individual taxpayers who have debt value until 31.12.2013 obtained from the database data warehouse and data mart IRBM. These data were analyzed using IBM SPSS version 16.0 applications. From the study of data mining framework and technique in theory, we choose CRISP-DM data mining life cycle framework include business understanding, data preparation, build models, evaluation and test the model as a framework and CHAID decision tree as technique. The results showed that the method CHAID Decision Tree in the model built to the study of type of test the value of 80% accurate. The result revealed that he is accurate model to use to predict future taxpayers which have outstanding debt with IRBM. 2015-07 Thesis https://ir.uitm.edu.my/id/eprint/63443/ https://ir.uitm.edu.my/id/eprint/63443/1/63443.pdf text en public masters Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences Hashim, Madziah (Prof.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Hashim, Madziah (Prof.)
topic	Credit Debt Loans Data mining
spellingShingle	Credit Debt Loans Data mining Mohamed Azidin, Hamisah Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin
description	This paper describes the development of a debt risk predictive model for individual taxpayer in Inland Revenue Board Malaysia (IRBM). Using data mining to predict taxpayer's compliance and non-compliance has gained attention in recent times. However, very little research has been done to predict taxpayers who have debt with tax organizations around the world. The objective of this study is to choose a suitable data mining methodology or framework and what is suitable data mining technique to build a predictive model debt risk for taxpayers who have debts with IRBM. This study also to get the behavior or pattern of data and identify important variables in predicting taxpayer with debt risk. Data individual taxpayers who have debt value until 31.12.2013 obtained from the database data warehouse and data mart IRBM. These data were analyzed using IBM SPSS version 16.0 applications. From the study of data mining framework and technique in theory, we choose CRISP-DM data mining life cycle framework include business understanding, data preparation, build models, evaluation and test the model as a framework and CHAID decision tree as technique. The results showed that the method CHAID Decision Tree in the model built to the study of type of test the value of 80% accurate. The result revealed that he is accurate model to use to predict future taxpayers which have outstanding debt with IRBM.
format	Thesis
qualification_level	Master's degree
author	Mohamed Azidin, Hamisah
author_facet	Mohamed Azidin, Hamisah
author_sort	Mohamed Azidin, Hamisah
title	Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin
title_short	Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin
title_full	Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin
title_fullStr	Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin
title_full_unstemmed	Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin
title_sort	developing debt risk model using data mining for irbm / hamisah mohamed azidin
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2015
url	https://ir.uitm.edu.my/id/eprint/63443/1/63443.pdf
_version_	1783735324506062848

Developing debt risk model using data mining for IRBM / Hamisah Mohamed Azidin

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