using dea for classification in credit scoring

نویسندگان

hoda golshani

department of mathematics, yadegar - e- imam khomeini (rah), shahr-e-rey branch, islamic azad university, tehran, iran. hadi bagherzadeh valami

department of mathematics, yadegar - e- imam khomeini (rah), shahr-e-rey branch, islamic azad university, tehran, iran. alireza davoodi

department of mathematics, neyshabur branch, islamic azad university, neyshabur, iran

چکیده

credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. for this sake, in this paper a data envelopment analysis- discriminant analysis (dea-da) approach is used for reclassifying  client  to reject or accept class for case of real data sets of  an iranian bank branch.  for this reason, two dea models are solved. also, the reject and accept frontiers and overlapping region among two frontiers are obtained.  then a goal programming problem is solved for finding co-efficients of the discriminant hyper-plane. the results are obtained from the samples are kept from the main dataset, clarify that the classified hyper-plane obtained from the used method provides an almost profitable classification for payment loan.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using DEA for Classification in Credit Scoring

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is us...

متن کامل

Domain Driven Classification of Customer Credit Data for Intelligent Credit Scoring using Fuzzy set and MC2

Credit scoring or credit risk assessment is an important research issue in the banking industry. The major challenge of credit scoring is to recruit the profitable customers by predicting the bankrupts. The credit scoring carried out by traditional data driven approaches resulted only in an imprecise solution. Also the domain-driven based multiple criteria and multiple constraint (MC2) level pr...

متن کامل

On Class Imbalance Correction for Classification Algorithms in Credit Scoring

Credit scoring is often modeled as a binary classification task where defaults rarely occur and the classes generally are highly unbalanced. Although many new algorithms have been proposed in the recent past to mitigate this specific problem, the aspect of class imbalance is still underrepresented in research despite its great relevance for many business applications. Within the “Machine Learni...

متن کامل

Using semi-supervised classifiers for credit scoring

In credit scoring, low-default portfolios are those for which very little default history exists. This makes it problematic for financial institutions to estimate a reliable probability of a customer defaulting on a loan. Banking regulation (Basel II Capital Accord), and best practice, however, necessitate an accurate and valid estimate of the probability of default. In this article the suitabi...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید


عنوان ژورنال:
international journal of data envelopment analysis

جلد ۴، شماره ۲، صفحات ۹۹۷-۱۰۰۵

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023