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Construction of classification models for credit policies in banks

Listed author(s):
  • Kuang-Hsun Shih
  • Hsu-Feng Hung
  • Binshan Lin

The execution and outcome of credit rating policies of banks are highly relevant to banks' decisions in investments, loans, and their measurement of default risks. They also have impacts on capital flows and utilisation of corporate borrowers. Therefore, it is essential to establish a scientific, objective, and accurate model of credit ratings for Customer Relationship Management (CRM) of the banking industry. The correct results of customers' ratings can serve as an important reference in the CRM of banks. This paper applies two classification methods, Multiple Discriminate Analysis (MDA) and Rough Set Theory (RST), to analyse and compare a total of 70 entries of corporate credit data. The result shows that the RST classification model boasts better classifying effects and is suitable for the analysis of credit ratings in the banking industry. This paper also suggests that a decision support system should be established with a scientific classification model in order to assist in the decisions and judgements of decision makers.

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Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Electronic Finance.

Volume (Year): 4 (2010)
Issue (Month): 1 ()
Pages: 1-18

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Handle: RePEc:ids:ijelfi:v:4:y:2010:i:1:p:1-18
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