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System No. U0002-1608201211100600
Title (in Chinese) 灰色傅立葉行為評等模式之建構
Title (in English) A Credit Cardholder Behavioral Scoring Model Using Residual Correction Fourier GM(1,1)
Other Title
Institution 淡江大學
Department (in Chinese) 管理科學學系碩士班
Department (in English) Master’s Program, Department of Management Sciences
Other Division
Other Division Name
Other Department/Institution
Academic Year 100
Semester 2
PublicationYear 101
Author's name (in Chinese) 林哲敏
Author's name(in English) Che-Min Lin
Student ID 699620778
Degree 碩士
Language English
Other Language
Date of Oral Defense 2012-06-21
Pagination 45page
Committee Member advisor - I-Fei Chen
co-chair - 歐陽良裕
co-chair - 李天行
Keyword (inChinese) 行為評等
灰色系統理論
灰色傅立葉
馬可夫鏈
貝氏機率
Keyword (in English) Behavioral Scoring
Grey System Theory
Grey Fourier
Markov chain
Bayesian
Other Keywords
Subject
Abstract (in Chinese)
本研究之目的是有鑒於金融機構中所隱藏之潛在信用風險,故藉由縮短顧客還款行為之觀察期,以建構預測顧客未來盈利能力之行為評等模式。藉由灰色理論善於處理短期資料之特性,我們建立GM(1,1)模型來縮短預測觀察期並驗證其用在分類資料的適用性,企圖降低銀行可能潛在的信用風險。然而,事實上GM(1,1)乃常用於預測面問題之解決,鮮少用於處理分類之相關議題上,導致其預測準確度不如預期,因此本研究將GM(1,1)模型結合具有週期性函數特性的傅立葉轉換,將其應用於殘差修正上,以提高模型的預測精確度。再者,由於傳統上馬可夫鏈被廣泛地應用在信用評等及行為評等上,故本研究利用馬可夫鏈模型之預測準確率視為灰色傅立葉模型之參考基準。最後,本文針對GM(1,1)模型,FGM模型,馬可夫鏈模型以及BGM(張雯琪,2011)等四個模型進行相關之比較,結果顯示FGM模型以及BGM模型皆有優異的預測準確率,且成功地將顧客還款行為之觀察期縮短為少於十期,此舉對於新申請帳戶之顧客,亦可達到快速制定授信決策之效,如此便能針對既有客戶提供更適切的服務,進而增加銀行之利潤。
Abstract (in English)
The purpose of this study is to construct the behavioral scoring model of predicting customer future profitability individual by shortening the period of observation his/ her payment profitability. Firstly, We construct GM(1,1) model to test the applicability of short-observation credit prediction associated with classification problems. Then, we proposed Fourier residual grey modification model (FGM) to improve the predictive accuracy. Next, we use Markov chain, a widely applied traditional method for solving credit/behavioral scoring problem, to provide a reference level of prediction accuracy. Finally, after comparing to GM, FGM, MC and GBM developed by Chang (2011), we find that the FGM(1,1) model and Bayesian grey model have outstanding performance of prediction accuracy. We find that the FGM(1,1) model and Bayesian grey model have outstanding performance of prediction accuracy and shorten the observation periods for less than 10 observations, successfully. This study delivers a managerial insight that the proposed model enables banks to take effect of the quick credit decisions, and then the financial institute can design appropriate marketing portfolios management based on the more accurately predicted status of customers future profitability.
Other Abstract
Table of Content (with Page Number)
TABLE OF CONTENTS	I
LIST OF TABLES	III
LIST OF FIGURES	IV
CHAPTER 1 INTRODUCTION	1
1.1 RESEARCH BACKGROUND	1
1.2 RESEARCH MOTIVATION AND PURPOSE	4
1.3 RESEARCH PROCESS	5
1.4 RESEARCH LIMITATION	7
CHAPTER 2 LITERATURE REVIEW	8
2.1 CREDIT SCORING AND BEHAVIORAL SCORING	8
2.2 GREY THEORY AND GREY MODIFICATION MODELS	10
2.3 MARKOV CHAINS	13
CHAPTER 3 METHODOLOGY	15
3.1 RESEARCH DESIGN	15
3.2 SUBJECTS	17
3.3 ANALYSIS APPROACH	19
3.3.1 Grey Prediction Model	19
3.3.2 Fourier Residual Grey Modification Model (FGM)	21
3.3.3 Bayesian Theorem	23
3.3.4 Markov Chain	24
CHAPTER 4 EMPRICAL	26
4.1 GM(1,1) MODEL	26
4.2 FOURIER RESIDUAL MODIFIED MODEL (FGM)	28
4.3 MARKOV CHAIN MODEL (MC)	29
4.4 COMPARISON OF CUSTOMER BEHAVIORAL SCORING MODELS	30
CHAPTER 5 CONCLUSIONS	35
5.1 CONCLUSIONS	35
5.2 RECOMMENDATIONS FOR FUTURE RESEARCH	37
REFERENCE	39

LIST OF TABLES
Table 1.1 Business of Credit Card in Domestic	3
Table 1.2 Assets Quality Analysis of Domestic Banks	5
Table 3.1 Description of Research Variables	18
Table 4.1 The Prediction Accuracy Rate of the GM Models on Testing Sample	27
Table 4.2 The Prediction Accuracy Rate of the FGM Models on Testing Sample	29
Table 4.3 The Prediction Accuracy Rate of the MC Models on Testing Sample	30
Table 4.4 Overview of Model Comparison on Accuracy Prediction	33
Table 4.5 Comparison of Prediction Accuracy Among	34
Table 4.6 Model Comparison of Prediction Accuracy Rate Using the Training Data of 8-Period (2)	34


LIST OF FIGURES
Figure 1.1 Trend of Unemployment Rent	3
Figure 1.2 Research Process	6
Figure 3.1 The Process of Constructing the Model	17
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