Study program
Second cycle study programme (Master level)
Branch: Financial and business mathematics
2nd cycle
Study level
Course title
Managing credit risk
Course code
MAT02-014
Language of instruction
English
Course description
Course objective. The objective of the course is to
make students familiar with basic concepts, tools and
models in credit and credit risk analysing. The course is
divided into three parts: (I) Introduction – including
definition of basic concepts regarding credits and credit
risks; (II) Credit risk models – including the overview
of certain credit risk models used in financial
institutions for making better business decisions, and
different input variables and methods used in
establishing these models. In this part students can also
find an overiew of methods used for developing models
without mathematical arguments and deductions,
including the explanation of the purpose of every
method; (III) Implementation – including an overview
of basic steps that are necessary to make in succesful
model implementation.
Prerequisites. Not required.
Course contents.
1. Credit policy: Trends in amount and quality
increase of credit financing. Credit trend in
Croatia and in the world. Changings in
population's and company's attitude towards
crediting.
2. Characteristics of different credit types. Real
estate credits. Commercial credits. Consumers
credits.
3. Types of risks. Credit risk. Liquidity risk.
Interest risk. Operational risk. Capital risk.
Currency risk.
4. The concept of credit risk: Necessity of
measuring credit risk. Defining credit risk. Basel
accord.
5. Classic credit analysis: Credit process. Credit
analysis. Evaluation of commercial credits.
Evaluation of consumer credits.
6. Introduction to credit risk models: Imperfections
of classic credit analysis. Need for credit risk
models. Importance of credit risk models in
better desicion making. Different ways in
application of credit risk models.
7. Methods in creating credit risk models
overview: Statistical methods. Neural networks.
Decision trees. Genetic algorithms.
8. Credit risk models based on accounting data:
Altman Z-score model, ZETA model. Other
statistical models and neural network models.
9. Credit risk models based on equity price: Option
price. EDF model. KMV model.
10. Consumers credit risk models. Criterias for
different consumers credits. Role of experts in
credit risk analysis. Quantitative models.
11. Smallbusiness credit risk models. Problems in
creating models for smallbusiness companies.
Importance and reasons for applying
smallbusiness models. RMA model.
Testing and implementation of credit risk models.
Quality of model. Stability of model. Monitoring
performances of model. Usage of model in decision
making process.
consultative teaching
Form of teaching
Students’ knowledge will be assessed on a regular
basis through tests and various assignments.
Furthermore, students have to pass the final
examination which will be in the written and the
oral form. Students are encouraged to work on the
project which will represent the application of
defining models on the example, and influence the
final grade.
Form of assessment
Number of ECTS
4
Class hours per week
2+0+2
Minimum number of
students
Period of realization
winter semester
Lecturer
Nataša Šarlija, Associate Professor