A Credit Cardholder Behavioral Scoring Model Using Residual Correction Fourier GM(1,1)

• Main Authors: Che-Min Lin, 林哲敏
• Other Authors: I-Fei Chen
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/11293031188448418467

碩士 === 淡江大學 === 管理科學學系碩士班 === 100 === 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.