Credit risk mangement in finance - a review of various approaches

• Authors: WÓJCICKA-WÓJTOWICZ Aleksandra
• Languages of publication: EN

Abstracts

EN

Classification of customers of banks and financial institutions is an important task in today’s business world. Reducing the number of loans granted to companies of questionable credibility can positively influence banks’ performance. The appropriate measurement of potential bankruptcy or probability of default is another step in credit risk management. Among the most commonly used methods, we can enumerate discriminant analysis models, scoring methods, decision trees, logit and probit regression, neural networks, probability of default models, standard models, reduced models, etc. This paper investigates the use of various methods used in the initial step of credit risk management and corresponding decision process. Their potential advantages and drawbacks from the point of view of the principles for the management of credit risk are presented. A comparison of their usability and accuracy is also made.

Keywords

EN

credit risk; default, bankruptcy; credit risk management; credit risk models

• Publisher: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
• Journal: Operations Research and Decisions
• Year: 2018
• Volume: 28
• Issue: 4
• Pages: 99-106

Contributors

author

WÓJCICKA-WÓJTOWICZ Aleksandra

• Department of Operations Research, Faculty of Informatics and Electronic Economy, University of Economics and Business, Aleja Niepodległości 10, 61-875 Poznań, Poland

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Identifiers

DOI

10.5277/ord180407