دانلود مقاله ISI انگلیسی شماره 48614
عنوان فارسی مقاله

مدل متغیر پنهان بیزی با طبقه بندی و رویکرد درخت رگرسیون برای امتیازدهی رفتاری و اعتباری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
48614 2012 8 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 36, December 2012, Pages 245–252

کلمات کلیدی
امتیازدهی رفتاری - اعتبارسنجی - بیزی - مدل متغیر پنهان - طبقه بندی و رگرسیون درخت
پیش نمایش مقاله
پیش نمایش مقاله مدل متغیر پنهان بیزی با طبقه بندی و رویکرد درخت رگرسیون برای امتیازدهی رفتاری و اعتباری

چکیده انگلیسی

A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately; (2) given current information about cardholders’ credit usage and repayment behavior, financial institutions would like to determine the optimal credit limit and APR for an applicant; and (3) the bank would like to improve its efficiency by automating the process of credit-granting decisions. Data from a leading bank in Taiwan is used to illustrate the combined approach. The data set consists of each credit card holder’s credit usage and repayment data, demographic information, and credit report. Empirical study shows that the demographic variables used in most credit scoring models have little explanatory ability with regard to a cardholder’s credit usage and repayment behavior. A cardholder’s credit history provides the most important information in credit scoring. The continuous latent customer quality from the Bayesian latent variable model allows considerable latitude for producing finer rules for credit granting decisions. Compared to the performance of discriminant analysis, logistic regression, neural network, multivariate adaptive regression splines (MARS) and support vector machine (SVM), the proposed model has a 92.9% accuracy rate in predicting customer types, is less impacted by prior probabilities, and has a significantly low Type I errors in comparison with the other five approaches.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.