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Credit Risk - methods of scorecards development in R

General data

Course ID:	2400-ZEWW752
Erasmus code / ISCED:	14.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0311) Economics The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Credit Risk - methods of scorecards development in R
Name in Polish:	Econometric modelling of binary variable - methods of Credit Risk scorecards development in R
Organizational unit:	Faculty of Economic Sciences
Course groups:	(in Polish) Przedmioty kierunkowe dla Data Science (in Polish) Przedmioty kierunkowe do wyboru - studia II stopnia EP - grupa 4 (130h) (in Polish) Przedmioty kierunkowe do wyboru - studia II stopnia FR - grupa 2 (230h) (in Polish) Przedmioty kierunkowe do wyboru - studia II stopnia IE - grupa 2 (2*30h) (in Polish) Przedmioty kierunkowe do wyboru- studia I stopnia EP (in Polish) Przedmioty wyboru kierunkowego dla studiów licencjackich FIR (in Polish) Przedmioty wyboru kierunkowego dla studiów licencjackich IE (in Polish) Przedmioty wyboru kierunkowego dla studiów licencjackich MSEM English-language course offering of the Faculty of Economics
ECTS credit allocation (and other scores):	3.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	optional courses
Short description:	The course gives both theoretical knowledge and practical skills to model a credit scorecard. During the course all necessary steps to develop a scorecard would be discussed and presented. Starting from data preparation (handling a missing data and outliers, derived variables preparation, data sampling), going through model estimation (i.e. logistic regression) and model quality assessment (discriminatory power, stability) and ending on optimal cut-off choice. During the course examples would be presented in R.
Full description:	A detailed course plan: 1. Statistical inference basics 2. Modelling sample definition 3. Risk factors specificity • Application data • Behavioral data 4. Data preparation • GB flag • Discretization and different methods of data preparation • Preliminary variable selection 5. Probability of Default prediction • Logistic regression and other methods 6. Method of scorecard building and transformation to Masterscale 7. Scorecard Quality assessment • Functional form selection • Goodness-of-fit tests • Discriminatory power • Stability analysis • Dimensions of Quality assessment 8. Optimal cut-off point choice
Bibliography:	Banasik, J., & Crook, J. (2004). Does reject inference really improve the performance of application scoring models? Journal of Banking & Finance, vol 28, pp. 857-874. Banasik, J., & Crook, J. (2007). Reject inference, augmentation, and sample selection. European Journal of Operational Research, 183 (2007) pp. 1582–1594. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Hoboken, NJ: Wiley. King, G., & Zeng, L. (2003). Logistic Regression in Rare Events Data. Journal of Statistical Software, 8(2). Kleinbaum, D. G., Klein, M., & Pryor, E. R. (2010). Logistic regression: a self-learning text. New York: Springer. Löffler, G., & Posch, P. N. (2013). Credit risk modeling using Excel and VBA. Chichester: John Wiley & Sons. Siddiqi, N. (2006). Credit risk scorecards developing and implementing intelligent credit scoring. Hoboken (N.J.): Wiley. Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit scoring and its applications. Philadelphia: Society for Industrial and Applied Mathematics.
Learning outcomes:	The students will learn how to perform a whole scorecard development project (from modeler perspective). Starting with data preparation (handling a missing data and outliers, derived variables preparation, data sampling), through model estimation (i.e. logistic regression) and model quality assessment (discriminatory power, stability) to optimal cut-off choice. KW01, KW02, KW03, KU01, KU02, KU03, KK01, KK02, KK03
Assessment methods and assessment criteria:	All students will be obliged to: • be present at the classes (according to common University of Warsaw rules), • prepare a project (code + paper) in which they will present a comparison of different scorecards quality

Classes in period "Summer semester 2023/24" (in progress)

Time span:	2024-02-19 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO TU W TH KON FR
Type of class:	Seminar, 30 hours more information
Coordinators:	Marcin Chlebus
Group instructors:	Marcin Chlebus
Students list:	(inaccessible to you)
Examination:	Course - Grading Seminar - Grading

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