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First part of this course covers most popular statistical models for credit risk scoring - logistic regression, decision
trees, gradient boosting method.
In following lectures, students will get familiar with procedures how to use scoring models in practice and how to
estimate risk of single loan and whole portfolios.
Emphasis will be put on the link between theoretical knowledge and procedures used in banking practice.
Last update: Zichová Jitka, RNDr., Dr. (16.05.2019)
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The objective of the lecture is to give an overview of the methods connected with credit risk management. The lecture will make students acquainted with the current trends in credit risk management.
Last update: Zichová Jitka, RNDr., Dr. (16.05.2019)
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Home assignments and oral exam. Home assignment will cover preparation of both classical and modern predictive models on real data. Last update: Kozmík Václav, RNDr., Ph.D. (26.09.2020)
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[1] Hosmer, David W. and Stanley Lemeshow, Applied Logistic Regression, 2nd ed., New York; Chichester, Wiley, 2000, ISBN 0-471-35632-8. [2] Chen T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, https://arxiv.org/abs/1603.02754 Last update: Kozmík Václav, RNDr., Ph.D. (26.09.2020)
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Lecture supported by slides. There is a study text which covers most of the lecture content. Last update: Kozmík Václav, RNDr., Ph.D. (27.09.2021)
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The requirements for the exams follow the syllabus of the course and they are limited to presented topics at the lectures. The exam consists of an oral examination. Last update: Vitali Sebastiano, Ph.D. (12.10.2017)
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1) Most popular statistical models for credit risk scoring - logistic regression, decision trees, gradient boosting method. 2) Procedures how to use scoring models in practice and how to estimate risk of single loan and whole portfolios. Emphasis will be put on the link between theoretical knowledge and procedures used in banking practice. Last update: Zichová Jitka, RNDr., Dr. (16.05.2019)
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Basic knowledge of mathematical statistics (particularly linear regression), theory of probability and mathematical analysis. For practical usage of the lecture content it is ideal to have basic knowledge of R or Python languages. Last update: Zichová Jitka, RNDr., Dr. (16.05.2019)
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