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  Citation Number 32
 Views 54
 Downloands 10
Kredi Riski Tahmininde Yapay Sinir Ağları ve Lojistik Regresyon Analizi Karşılaştırılması
2012
Journal:  
AJIT-e: Academic Journal of Information Technology
Author:  
Abstract:

Kredi değerlendirme, finansal sistemdeki sınırlı kaynakların daha verimli kullanılabilmesi açısından, bankalar için oldukça önemli bir konudur. Bankalar, kredi değerlendirmesinde bir çok yöntem kullanmaktadır. Bu yöntemlerden bir tanesi de, kredi talep eden müşterinin kredisini düzenli ödeyip ödemeyeceğinin tahmin edilmesidir. Bu çalışmada, bankaların kredi risklerini öngörmelerine yardımcı olması amacıyla, kredi talep eden müşterilerin ödeme alışkanlıklarının düzenli olup olmayacağının tahmin edilmesi için yapay sinir ağları ve lojistik regresyon analizi kullanılmıştır. Çalışma sonucunda, yapay sinir ağı yönteminin müşterilerin ödeme alışkanlıklarının düzenli olup olmayacağını tahmin etme gücü lojistik regresyon modelinden daha üstün olduğu tespit edilmiştir.

Keywords:

comparison of artificial nerve networks and logistic regression analysis in credit risk forecast
2012
Author:  
Abstract:

credit assessment is a rather important issue for banks in terms of the limited resources in the financial system, banks use a lot of methods in credit assessment, one of these methods is predicting that the client who demand credit will regularly pay and pay the credit of the loan in this study, in order to help banks predict their credit risks, artificial nerve networks and logistic regression analysis was used as a result of the study, the artificial nervous network method is superior to the model that the customers will not be regular of payment habits

Keywords:

Comparison Of Artificial Neural Networks and Logistic Regression Analysis In The Credit Risk Prediction
2012
Author:  
Abstract:

Credit scoring is a vital topic for Banks since there is a need to use limited financial sources more effectively. There are several credit scoring methods that are used by Banks. One of them is to estimate whether a credit demanding customer’s repayment order will be regular or not. In this study, artificial neural networks and logistic regression analysis have been used to provide a support to the Banks’ credit risk prediction and to estimate whether a credit demanding customers’ repayment order will be regular or not. The results of the study showed that artificial neural networks method is more reliable than logistic regression analysis while estimating a credit demanding customer’s repayment order.

Keywords:

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AJIT-e: Academic Journal of Information Technology

Journal Type :   Uluslararası

Metrics
Article : 332
Cite : 1.893
2023 Impact : 0.471
AJIT-e: Academic Journal of Information Technology