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2015 | OriginalPaper | Chapter

Decision Models in Credit Risk Management

Authors : Herbert Kimura, Leonardo Fernando Cruz Basso, Eduardo Kazuo Kayo

Published in: Decision Models in Engineering and Management

Publisher: Springer International Publishing

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Abstract

Economic crises that emerge from systemic risks suggest that credit risk management in banks is paramount not only for the survival of companies themselves but also for a resilient worldwide economy. Although regulators establish strictly standards for financial institutions, i.e., capital requirements and management best practices, unpredictability of market behavior and complexity of financial products may have strong impact on corporate performance, jeopardizing institutions, and even economies. In this chapter, we will explore decision models to manage credit risks, focusing on probabilistic and statistical methods that are coupled with machine learning techniques. In particular, we discuss and compare two ensemble methods, bagging and boosting, in studies of application scoring.

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previous chapter Multi-attribute Utility Model Based on the Maximum Entropy Principle Applied in the Evaluation of the Financial Performance of Brazilian Banks
next chapter A Review on the Dynamic Decision Models for Manufacturing and Supply Chain
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Metadata
Title
Decision Models in Credit Risk Management
Authors
Herbert Kimura
Leonardo Fernando Cruz Basso
Eduardo Kazuo Kayo
Copyright Year
2015
Book
Decision Models in Engineering and Management

Print ISBN: 978-3-319-11948-9

Electronic ISBN: 978-3-319-11949-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-11949-6_4