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Credit Risk in Banking - NMFP461

Title:	Kreditní riziko v bankovnictví
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2024
Semester:	winter
E-Credits:	3
Hours per week, examination:	winter s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time

Guarantor:	RNDr. Václav Kozmík, Ph.D.
Teacher(s):	RNDr. Václav Kozmík, Ph.D. Marek Teller
Class:	M Mgr. FPM M Mgr. FPM > Volitelné M Mgr. PMSE M Mgr. PMSE > Volitelné
Classification:	Mathematics > Financial and Insurance Math.
Incompatibility :	NMFM537
Interchangeability :	NMFM537
Is incompatible with:	NMFM537
Is interchangeable with:	NMFM537

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

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: Branda Martin, doc. RNDr., Ph.D. (09.12.2020)

Aim of the course -

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. (18.05.2022)

Course completion requirements -

Home assignments and oral exam. Home assignment will cover preparation of both classical and modern predictive models on real data.

Last update: Zichová Jitka, RNDr., Dr. (18.05.2024)

Literature -

[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: Kopa Miloš, doc. RNDr. Ing., Ph.D. (09.12.2020)

Teaching methods -

Lecture.

Last update: Zichová Jitka, RNDr., Dr. (18.05.2022)

Requirements to the exam -

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: Zichová Jitka, RNDr., Dr. (18.05.2024)

Syllabus -

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: Kopa Miloš, doc. RNDr. Ing., Ph.D. (09.12.2020)

Entry requirements -

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. (18.05.2024)