Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
One of the major premises used in credit scoring is that “the future is like the past”. It’s usually a rational assumption and gives us a reasonable platform on which to build scorecards whether they be application scorecards, behavioural scores, collection scores or financial models. That is reasonable until something unprecedented comes along. You can read about this black swan event in our previous two blogs here and here
This is the second of a 2-part blog. You can read the first blog here.
One of the basic principles of credit scoring and modelling is that the “future is like the past”. Whilst robust credit models may be calibrated on multiple time periods, this assumes that trends in the past represent what is going on today. COVID-19 is a black swan event – meaning in the modern day it really is unprecedented. If you have never come across the term black swan, or if you have but no idea the origin, I recommend taking two minutes to read its really interesting etymology.
With a recent judgment being upheld in favour of the National Credit Regulator (NCR) against Shoprite Investments Limited, we thought it would be a good time to re-look at the process of affordability assessment.
At Principa, we are passionate about new ideas and product development. 20% of our revenue is ploughed back into innovation. One of our key areas of focus is machine learning where we have built machine learning solutions in both collections and the customer acquisition space. While our focus is primarily on credit risk and customer engagement, we are always interested in how machine learning has gained traction in other industries.