Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)

In July 2014, the International Accounting Standards Board (IASB) delivered a new directive on how to recognize and measure financial instruments as a continuous effort to increase financial stability across the globe. The new International Financial Reporting Standard or IFRS9 which includes requir...

Full description

Saved in:

Bibliographic Details
Main Author:	Yosi Lizar, Eddy
Format:	Thesis
Language:	eng eng eng
Published:	2021
Subjects:	HG Finance
Online Access:	https://etd.uum.edu.my/9519/1/depositpermission-not%20allow_s95131.pdf https://etd.uum.edu.my/9519/2/s95131_01.pdf https://etd.uum.edu.my/9519/3/s95131_02.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.9519
record_format	uketd_dc
spelling	my-uum-etd.95192022-06-26T01:33:26Z Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9) 2021 Yosi Lizar, Eddy Engku Abu Bakar, Engku Muhammad Nazri Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences HG Finance In July 2014, the International Accounting Standards Board (IASB) delivered a new directive on how to recognize and measure financial instruments as a continuous effort to increase financial stability across the globe. The new International Financial Reporting Standard or IFRS9 which includes requirements for recognition and measurement, impairment and general hedge accounting will supersede the older version of FRS139 in phases and mandatorily implemented at the periods beginning of 1st January 2018. However, the implementation of IFRS9 depends on the strategy imposed by the respective bank to secure their business goal. Motivated by this standard, this study constructs a mathematical credit scoring model that aligns and adheres with this new impairment standard outlined by IASB by incorporating a forward-looking expected credit loss from the initial origination date of financing. This proposed model simplifies and strengthens risk measurement and the reporting of financial instruments by considering the time value of money and cost amortization as required under IFRS9 guidelines. Then, the model computes the probability of default based on credit risk of individual evaluation attributes and anticipates future credit risk deterioration with the use of available historical, current and forecasted macroeconomic variables. Empirical evidence recorded from the analysis performed on six years financial data shows promising results in respect to low error rate in distinguishing between default and non-default creditors. In addition, statistical analyses conducted for model adequacy check, model goodness-of-fit test and model validation indicate that the model is adequate. The model is much reliable than the traditional risk profile model and is able to assist the financial institutions in identifying group of future creditors accurately. 2021 Thesis https://etd.uum.edu.my/9519/ https://etd.uum.edu.my/9519/1/depositpermission-not%20allow_s95131.pdf text eng staffonly https://etd.uum.edu.my/9519/2/s95131_01.pdf text eng staffonly https://etd.uum.edu.my/9519/3/s95131_02.pdf text eng staffonly other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Engku Abu Bakar, Engku Muhammad Nazri
topic	HG Finance
spellingShingle	HG Finance Yosi Lizar, Eddy Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)
description	In July 2014, the International Accounting Standards Board (IASB) delivered a new directive on how to recognize and measure financial instruments as a continuous effort to increase financial stability across the globe. The new International Financial Reporting Standard or IFRS9 which includes requirements for recognition and measurement, impairment and general hedge accounting will supersede the older version of FRS139 in phases and mandatorily implemented at the periods beginning of 1st January 2018. However, the implementation of IFRS9 depends on the strategy imposed by the respective bank to secure their business goal. Motivated by this standard, this study constructs a mathematical credit scoring model that aligns and adheres with this new impairment standard outlined by IASB by incorporating a forward-looking expected credit loss from the initial origination date of financing. This proposed model simplifies and strengthens risk measurement and the reporting of financial instruments by considering the time value of money and cost amortization as required under IFRS9 guidelines. Then, the model computes the probability of default based on credit risk of individual evaluation attributes and anticipates future credit risk deterioration with the use of available historical, current and forecasted macroeconomic variables. Empirical evidence recorded from the analysis performed on six years financial data shows promising results in respect to low error rate in distinguishing between default and non-default creditors. In addition, statistical analyses conducted for model adequacy check, model goodness-of-fit test and model validation indicate that the model is adequate. The model is much reliable than the traditional risk profile model and is able to assist the financial institutions in identifying group of future creditors accurately.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Yosi Lizar, Eddy
author_facet	Yosi Lizar, Eddy
author_sort	Yosi Lizar, Eddy
title	Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)
title_short	Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)
title_full	Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)
title_fullStr	Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)
title_full_unstemmed	Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)
title_sort	enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (ifrs9)
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2021
url	https://etd.uum.edu.my/9519/1/depositpermission-not%20allow_s95131.pdf https://etd.uum.edu.my/9519/2/s95131_01.pdf https://etd.uum.edu.my/9519/3/s95131_02.pdf
_version_	1747828613764349952

Enhanced predictive credit scoring model for customer's default detection based on new international financial reporting standard (IFRS9)

Similar Items