ANN Model for Corporate Credit Risk Assessment

Author

Dima, Alina Mihaela ; Vasilache, Simona

Author_Institution

Acad. of Economic Studies, Bucharest, Romania

fYear

2009

fDate

17-20 April 2009

Firstpage

94

Lastpage

98

Abstract

The paper proposes decision tools to be used by commercial banks in order to classify the companies applying for credits in good and bad creditors, based on the number of days of delay in payment. Probit regression and neural networks, applied to a sample of companies which delayed or not their credit reimbursements are used to orient the decision taken by the bank, consistent with its priorities, of either minimizing the risk, or enlarging the customer base.

Keywords

artificial intelligence; banking; decision support systems; neural nets; regression analysis; risk management; artificial neural networks; commercial banks; corporate credit risk assessment; credit reimbursements; customer base; decision tools; regression analysis; Algorithm design and analysis; Classification tree analysis; Econometrics; Economic forecasting; Hazards; Logistics; Neural networks; Performance analysis; Predictive models; Risk management; artificial intelligence; credit risk; probit regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Financial Engineering, 2009. ICIFE 2009. International Conference on

Conference_Location

Singapore

Print_ISBN

978-0-7695-3606-4

Type

conf

DOI

10.1109/ICIFE.2009.33

Filename

5189976