Posted
06 Dec 2017Category
Data ScienceShare
To complete our recent blog series about credit scoring, here is a short video introducing the WPS Data Science Module (DSM), a toolset capable of building behavioural and application scorecards. DSM features a collection of point-and-click tools including a Workflow Editor, Data Profiler and Machine Learning capabilities.
Please get in touch if you would like to know more about DSM or about credit scoring.
- Why do credit scoring? What is a credit score?
- How I make sure that other data scientists can replicate the same steps and produce similar results
- Data preparation is a key aspect of any data mining project including development of a credit scorecard
- How to achieve parsimony and what is the key information to look for?
- Scorecard development describes how to turn data into a scorecard model
- Segmentation and reject inference, or keep it simple? – That is the question!
- Advanced validation framework and dealing with unbalanced data
- Credit risk strategy is the process that follows after the scorecard development and before its implementation. It tells us how to interpret the customer score and what would be an adequate actionable treatment corresponding to that score.
- The real benefit of a scorecard or a credit strategy is only evident on implementation.
- By putting the previous pieces together, we start building a bigger picture of the enterprise decision management (EDM) system.
