Processing Of Large Amounts Of Data On A
Credit Scoring Example Using Neural Network
Technology
Author(s)
K. K. Nurlybayeva & G. T. Balakayeva
Abstract
Nowadays there is the growing problem of mining large amounts of data. This
article is dedicated to the issue of development of a credit scoring model as an
example of processing large volumes of data. Some data mining algorithms are
described in the paper. Three methods have been used for the experiment;
namely, logistic regression, decision trees and neural networks. All of them have
been applied for the modeling of credit scoring. According to the results of a
comparative analysis, neural networks have been selected as a technology for the
credit scoring model design. The main aim is to choose the best method of data
mining and construction of predictive credit scoring without using expensive
software, together with the ability for self-learning and updating. To implement
and achieve the goal, the following tasks have been undertaken: collecting and
preparing the initial data, analysis and selection of available technologies and
methods of solution, to determine the most suitable method of data mining to
build a credit scoring system, and now the project is on the way to creating an
expert system.
Keywords: data mining, logistic regression, decision trees, neural networks,
scoring model, credit scoring system.
Keywords
data mining, logistic regression, decision trees, neural networks,
scoring model, credit scoring system
Related Book
Other papers in this volume
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