Unit 1 – The Context of Statistical Credit Scoring
All the buzzwords explained: credit bureau scoring, expert scoring, credit rating, application scoring, behavioral scoring, scoring on alternative data: mobile, social media and psychometric observations. The ethics of scoring. How to address data privacy and data confidentiality concerns.
Unit 2 – The Statistical Scoring Process
Defining the analytical target. Setting the good/bad variable. Selecting the relevant population of observations in terms of time window, sectors, geographies. Generating and cleaning up the data set. Dealing with outliers and missing values. Coding, discretizing / transforming / recombining the candidate variables. Estimating a model using various algorithms. Validation testing on the reserved sample.
Unit 3 – Recalibration and Maintenance
Test and recalibrate existing models periodically for continued performance. Integrating new seasoned observations and additional data points. Making forward looking expert adjustments in order to capture external effects, such as the medium-term economic impact of the COVID-19 pandemic.
Unit 4 – Outlook: What else can we score?
Practical example of deploying scoring for fraud prevention. Plus additional use cases for scoring using ideas contributed by participants in the course.
