A NEURAL NETWORK APPROACH FOR BANK CREDIT DECISIONS (Podejmowanie decyzji kredytowych w oparciu o sztuczne sieci neuronowe)

• Authors: Rubin Beata
• Languages of publication: PL

Abstracts

EN

To grant a bank credit or not is very important for effective bank management. A standard approach of computer decision support systems is based on spread sheets. The artificial neural network approach gives more possibilities to analyze credit decisions, to classify entities applying for credits according to their credibility, including different groups of risk. In this work, the authoress presents a review of various neural network topologies appliance and net trained using various algorithms for dichotomous and polytomic classification. Classification errors were compared and the most effective net was determined. Advantages and disadvantages of described method were shown, which indicate the right appliance of artificial neural networks for the analysis of loan debtors. The usage of artificial networks can rationalize and speed up the process of granting credits, as well as provide a basis for a secondary verification of refused applications.

Keywords

EN

CREDIT CAPACITY; CREDIT RISK; DISCRIMINATORY ANALYSIS; NEURAL NETWORK

Discipline

• ECONOMICS: ECONOMICS

• Journal: Optimum. Studia Ekonomiczne
• Year: 2010
• Issue: 1(45)
• Pages: 138-152
• Document type: ARTICLE

Contributors

author

Rubin Beata

• Beata Rubin, Uniwersytet w Bialymstoku, Wydzial Ekonomii i Zarzadzania, ul. Warszawska 63, 15-062 Bialystok, Poland

Identifiers

CEJSH db identifier

10PLAAAA08609