Credit Risk - methods of scorecards development in R 2400-ZEWW752
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
Type of course
Course coordinators
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 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
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.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: