Archives and Documentation Center
Digital Archives

A modular approach for SMEs credit risk analysis

Show simple item record

dc.contributor Graduate Program in Computer Engineering.
dc.contributor.advisor Gürgen, Fikret.
dc.contributor.author Derelioğlu, Gülnur.
dc.date.accessioned 2023-03-16T09:59:50Z
dc.date.available 2023-03-16T09:59:50Z
dc.date.issued 2009.
dc.identifier.other CMPE 2009 D47
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12127
dc.description.abstract Credit risk analysis is a challenging problem in financial analysis domain. It aims to estimate the risk occurred when a customer is granted. The risk estimation depends on both customer behavior and economical condition. The challenge is how the credit expert will determine which information should be collected from applicants, under which condition a customer will be classified as good and how much risk will be taken if the credit is granted to the customer. Consequently, credit experts need intelligent customer-specific risk analysis modules to support them when they make these decisions. In this thesis, we present a cascaded multilayer perceptron (MLP) rule extractor and a logistic regression (LR) model a for real-life Small and Medium Enterprises (SMEs). In the preprocessing phase, the features of Turkish SME database are selected by decision tree (DT), recursive feature extraction (RFE), factor analysis (FA) and principal component analysis (PCA) methods. The best feature set is obtained by RFE. In the first module, the classifier is selected among MLP, k-nearest neighbor (KNN) and support vector machine (SVM). The optimal classifier is obtained as MLP and the following modules are built on MLP. For classification purpose, MLP is followed by neural rule extractor (NRE) in the second module. NRE reveals how the decision is made for customers as being “good”. For the probability of default estimation (PD), we propose a cascaded MLP which is followed by a LR model in the third module. MLP-LR model is followed by clustering method in the last module for scorecard development purpose. In experiments, confidential Turkish SME database is used. The cascaded MLP-LR model provides high accuracy rate and outperforms commonly used classical LR.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2009.
dc.subject.lcsh Credit analysis.
dc.subject.lcsh Risk assessment.
dc.subject.lcsh Logistic regression analysis.
dc.title A modular approach for SMEs credit risk analysis
dc.format.pages xii, 66 leaves;


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Digital Archive


Browse

My Account