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Short Course
Monitoring and Validation of Credit Scoring Models
The financial industry currently has a shortage of people with the necessary data science skills to address problems relating to large and/or complex data sets. Nowadays financial institutions are confronted with an abundance of data, and so in order to maintain a competitive advantage, it is imperative to have analysts with these necessary data science skills. This short course will address one of the topics needed to become a skillful data scientist.

Purpose of the course:

This short course aims to equip participants with the general, high level skills necessary for the validation and monitoring of credit scoring models through the use of practical examples with a focus on retail credit.

Admission requirements:

Admission requirements: 
NQF level 7 majoring in either Mathematics/Statistics/Engineering/Computer Science.
Learning assumed to be in place: 
NQF level 7 Previous knowledge on credit scoring or predictive modeling is recommended.

Course outcomes and assessment criteria :

Course outcomes and the associated assessment criteria: 

Study Unit


Assessment Criteria

  After completion of this course, participants will: Participant will be assessed on the following criteria:
  1. Demonstrate applied knowledge and understanding of monitoring and validation of credit scorecards and understand how it is applied in practice. 
  2. Demonstrate an ability to identify real-world practical problems related to the monitoring and validation of credit scorecards and methods to solve these problems. 
  3. Demonstrate an ability to use a range of monitoring (e.g. Gini, PSI, Rank ordering, CSI) and validation (e.g. stability, discrimination, calibration) techniques to identify, analyse and address complex problems.
  1. Perform basic validation techniques (e.g. stability, discrimination, calibration).
  2. Evaluate and analyse basic validation results (specifically within the context of back-testing).
  3. Perform basic monitoring by implementing various measures (e.g. Gini, PSI, Rank ordering, CSI).
  4. Evaluate and analyse basic monitoring results within the credit scoring environment.


Assignments and participation.
Method of assessment: 
Learning objectives will be accomplished through the successful completion of assignments and participation in short course activities, proving insight into the topics at hand.

Additional information

Mode of delivery: 
Target group: 
Graduates and/or industry professionals working in fields that require data science and analytical skills.
Contact us
Prof Tanja Verster
Telephone number: 
018 299 2566