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系統識別號 U0002-1608201211100600
中文論文名稱 灰色傅立葉行為評等模式之建構
英文論文名稱 A Credit Cardholder Behavioral Scoring Model Using Residual Correction Fourier GM(1,1)
校院名稱 淡江大學
系所名稱(中) 管理科學學系碩士班
系所名稱(英) Master’s Program, Department of Management Sciences
學年度 100
學期 2
出版年 101
研究生中文姓名 林哲敏
研究生英文姓名 Che-Min Lin
學號 699620778
學位類別 碩士
語文別 英文
口試日期 2012-06-21
論文頁數 45頁
口試委員 指導教授-陳怡妃
委員-歐陽良裕
委員-李天行
中文關鍵字 行為評等  灰色系統理論  灰色傅立葉  馬可夫鏈  貝氏機率 
英文關鍵字 Behavioral Scoring  Grey System Theory  Grey Fourier  Markov chain  Bayesian 
學科別分類
中文摘要 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。
英文摘要 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.
論文目次 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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