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\LARGE {\bf Review of Neural Network Application to Creditworthiness Scoring}
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\large {SURNAME Name$^1$ and \underline{SURNAME Name}$^2$}
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\it {$^1$Institute for Research and Applications of Fuzzy Modelling, NSC IT4Innovations, University of Ostrava,\\
30. dubna 22, 701 03 Ostrava 1, Czech Republic,\\
Soheyla.Mirshahi@gmail.com\\
$^2$Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam\\
Surname2@tdtu.edu.vn}
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\textbf{Please underlining the one who will give the talk!!!!!!!!!!!!!!!}
The creditworthiness measurement of borrowers is a critical problem in bank sectors and financial institutes. Different techniques such as linear discriminant analysis, logistic regression, $k$ nearest neighbor, kernel density estimation, decision trees, genetic programming etc, have been used for measuring the creditworthiness of borrowers \cite{ref1}. Neural network is another technique which has been applied for solving this problem during the last few decades \cite{ref2,ref3}. The principal aim of this paper is to carry out a comprehensive review of different neural network applications which is used for credit scoring.
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\bibitem{ref1} H. A. Abdou, J. Pointon: {\it Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature.} Intelligent Systems in Accounting, Finance and Management, (2011), pp. 59--88.
\bibitem{ref3}
H. L. Jensen: {\it Using Neural Networks for Credit Scoring.} Managerial Finance, {\bf 18} Issue: 6, (1992), pp.15-26
\bibitem{ref2}
L. Yijun, C. Qiuru, L. Ye: {\it Artificial Neural Networks for Corporation Credit Rating Analysis.} International Conference on Networking and Digital Society, 2009, DOI: 10.1109/ICNDS.2009.26
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