Using DEA for Classification in Credit Scoring
Authors
Abstract:
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.
similar resources
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...
full textDomain 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...
full textOn 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...
full textUsing 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...
full textMy Resources
Journal title
volume 4 issue 2
pages 997- 1005
publication date 2016-05-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023