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Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard

Machine Learning models have been extensively researched in the area of credit scoring. Banks have put in substantial resources into improving the credit risk model performance as improvement in accuracy by a fraction could translate into significant future savings. Given the lack of interpretabilit...

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Main Author: Lee, Choon Yi
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Q Science (General)
Online Access:http://eprints.utar.edu.my/4592/1/Lee_Choon_Yi.pdf
http://eprints.utar.edu.my/4592/
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spelling my-utar-eprints.45922022-08-25T13:46:01Z Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard Lee, Choon Yi Q Science (General) Machine Learning models have been extensively researched in the area of credit scoring. Banks have put in substantial resources into improving the credit risk model performance as improvement in accuracy by a fraction could translate into significant future savings. Given the lack of interpretability in machine learning models, it is often not used for capital provisionin g in banks. This paper uses the Taiwan Credit Card dataset and illustrates the use of machine learning techniques to improve assessment of credit worthiness using credit scoring models. In factor transformation for a credit scorecard construction, Decision Tree technique showed the ability to produce quick and predictive transformation rule. Besides, model comparison result showed that Artificial Neural Network and Gradient Boosting Approach have great predictive power compared to traditional logistic regre ssion scorecard . Credit underwriting decision could be improved by implementing a better discriminatory power scorecard, as more good customers are likely to be better than score cut thus accepted by banks. Probability of Default (PD) Calibration moff and aps model scores to output PD that reflects portfolio underlying performance. This paper illustrates approach to perform PD calibration for machine learning models that can be used to align with banks internal application scorecard strategy . Calibration Plot and Binomial Test assessment showed that traditional scorecard approach performed better with least risk of underestimation of actual PD . Both tests suggest estimation purpose. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4592/1/Lee_Choon_Yi.pdf Lee, Choon Yi (2022) Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard. Master dissertation/thesis, UTAR. http://eprints.utar.edu.my/4592/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
spellingShingle Q Science (General)
Lee, Choon Yi
Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard
description Machine Learning models have been extensively researched in the area of credit scoring. Banks have put in substantial resources into improving the credit risk model performance as improvement in accuracy by a fraction could translate into significant future savings. Given the lack of interpretability in machine learning models, it is often not used for capital provisionin g in banks. This paper uses the Taiwan Credit Card dataset and illustrates the use of machine learning techniques to improve assessment of credit worthiness using credit scoring models. In factor transformation for a credit scorecard construction, Decision Tree technique showed the ability to produce quick and predictive transformation rule. Besides, model comparison result showed that Artificial Neural Network and Gradient Boosting Approach have great predictive power compared to traditional logistic regre ssion scorecard . Credit underwriting decision could be improved by implementing a better discriminatory power scorecard, as more good customers are likely to be better than score cut thus accepted by banks. Probability of Default (PD) Calibration moff and aps model scores to output PD that reflects portfolio underlying performance. This paper illustrates approach to perform PD calibration for machine learning models that can be used to align with banks internal application scorecard strategy . Calibration Plot and Binomial Test assessment showed that traditional scorecard approach performed better with least risk of underestimation of actual PD . Both tests suggest estimation purpose.
format Final Year Project / Dissertation / Thesis
author Lee, Choon Yi
author_facet Lee, Choon Yi
author_sort Lee, Choon Yi
title Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard
title_short Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard
title_full Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard
title_fullStr Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard
title_full_unstemmed Credit Risk Prediction Using Calibration Method: An Application In Financial Scorecard
title_sort credit risk prediction using calibration method: an application in financial scorecard
publishDate 2022
url http://eprints.utar.edu.my/4592/1/Lee_Choon_Yi.pdf
http://eprints.utar.edu.my/4592/
_version_ 1744358167381278720
score 13.211869

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