The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

Man Vs. Machine-Learning With Ben Karpinski Of CliffCentral

October 19, 2015 at 12:50 PM

Interview on Machine Learning with Ben Karpinski

Here's a follow-up to Friday's chat with Ben Karpinski and Gareth Cliff from CliffCentral about our prediction for a win for the Springboks by 4 points. Our use of Machine Learning and Predictive Analytics to predict the win resulted in a prediction that was spot-on. However, Ben's overall predictions for all 4 matches this past weekend were more accurate with him having predicted a win by Argentina against Ireland and our algorithms predicting the opposite.

#ManVMachine-Learning: Ben 1, Principa 1 

Listen here to Ben's analysis and chat with us here. Fast forward to minute 43.

Using machine learning in business - download guide

Latest Posts

The time is NOW for model validation and adjustment.

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

10 ways the COVID-19 crisis will affect your credit models (PART 2)

This is the second of a 2-part blog. You can read the first blog here.

10 ways the COVID-19 crisis will affect your credit models (PART 1)

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.