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Consumer Loan Credit Risk Analyzer Using Artificial Neural Networks

Published on December 2013 by Shilpa Laddha, Shubhangi Sapkal, Anjali Kulkarni
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National Conference on Innovative Paradigms in Engineering & Technology 2013
Foundation of Computer Science USA
NCIPET2013 - Number 1
December 2013
Authors: Shilpa Laddha, Shubhangi Sapkal, Anjali Kulkarni
54ec9223-b428-4cd8-a70f-e8b49d672562

Shilpa Laddha, Shubhangi Sapkal, Anjali Kulkarni . Consumer Loan Credit Risk Analyzer Using Artificial Neural Networks. National Conference on Innovative Paradigms in Engineering & Technology 2013. NCIPET2013, 1 (December 2013), 6-10.

@article{
author = { Shilpa Laddha, Shubhangi Sapkal, Anjali Kulkarni },
title = { Consumer Loan Credit Risk Analyzer Using Artificial Neural Networks },
journal = { National Conference on Innovative Paradigms in Engineering & Technology 2013 },
issue_date = { December 2013 },
volume = { NCIPET2013 },
number = { 1 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 6-10 },
numpages = 5,
url = { /proceedings/ncipet2013/number1/14693-1305/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Innovative Paradigms in Engineering & Technology 2013
%A Shilpa Laddha
%A Shubhangi Sapkal
%A Anjali Kulkarni
%T Consumer Loan Credit Risk Analyzer Using Artificial Neural Networks
%J National Conference on Innovative Paradigms in Engineering & Technology 2013
%@ 0975-8887
%V NCIPET2013
%N 1
%P 6-10
%D 2013
%I International Journal of Computer Applications
Abstract

Neural networks provide an obvious technique for classification. In this paper, neural network approach is used for classification of Loan applications. Data has been collected from publicly available source, Alyuda Research, Inc. [1]. It does not guarantee the accuracy of the data. This data is intended solely for experimental purposes. FFNN and RBN are used for classification. PREMNMX matlab Function Preprocesses data so that minimum is -1 and maximum is 1

References
  1. Historical Dataset, Alyuda Research, Inc.
  2. Loretta J. Mester, "What's the Point of Credit Scoring?, Business Review, (Oct 1997 ), pp. 1-14.
  3. Clarence N. W. Tan and Gerhard E. Wittig, "A Study of the Parameters of a Backpropagation Stock Price Prediction Model" 1993. ,pp. 288 – 291.
  4. Ying-Hua Lu; Chun-Guo Wu; Yan-Chun Liang, "Center selection for RBF NN in prediction of nonlinear time series" 2003, pp. 1355 - 1359 .
  5. Website:http://www. virtualventures. ca/~neil/neural/neuron-a. html
  6. Yegya Narayana, "Artificial Neural Network", PHI.
  7. Gose, "Pattern Recognition and Image Analysis"
  8. Duda Hart, "Pattern Classification", 2 e,Wiley
  9. Fabrizio De Nittis "Consumer Loan Classification Using Artificial Neural Networks" ICSC EIS'98 conference.
  10. A Guide to MATLAB: Brian R. Hunt.
  11. Fundamentals of Artificial Neural Networks Mohamad Hassoun.
  12. Back Propagation: Theory, Architectures, and Applications, David E. Rumelhart.
Index Terms

Computer Science
Information Sciences

Keywords

Feedforward Neural Network radial Basis Network loan Approval.