A three-day course with hands-on computer exercises on how to build scorecards.
- Overview of scorecard development process
- Analysis and transformation of characteristics
- Logistic regression: theory and practice
- Selection of characteristics for building scoring models
- Methods of assessing of predictive power of scoring systems
- Calculation of scorepoints
- Reject inference: taking into account rejected applications
- Using of scoring systems
The training bases on presentations and hands-on exercises with R. Thanks to this fact you will be able to work on your own data using this tool. As your experience will grow you will prepare your own set of methods and R functions for building models. It will suit you: effective, convenient, and using powerful methods -- methods you like to use and appropriate for specificity of credit portfolios and data you work with. Even if you don't use R you will benefit from the training. The methods introduced are best practices and are accessible in many statistical tools. R aims as an illustrative facility. Thanks to using R in the training you will get knowledge and practical skills independent of commercial software.
There will be a lot of hands-on computer exercises. Participants are required to bring laptops. We will use RStudio through a web browser. It means that you will need just a web browser and MS Excel.
What will you learn?
- You will be able to build a scoring model. Even if you will start without any knowledge in this topic.
- Learn all the stages of the scoring system development process: starting from gathering data, through selection of best features, determination of scoring points, quality assessments, up to monitoring of a working system.
- Learn how to preprocess data for development of scoring systems.
- What are statistical methods that are applied there?
- Learn how to solve problem of lack of information about rejected applications (reject inference).
- Get knowledge and skills in assessment of quality of scoring models.
- Learn essential basics of R.
- Work on these topics hands-on with a computer: we use R and RStudio.