Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
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.
Whether you’ve been involved in introducing models into your business or have had a passing interest in economic affairs, you may have come across the term “Gini-coefficient”. This blog hopes to demystify the concept and give you a good deal of information on the statistical measurement. We answer:
Collection departments utilise diallers and collections management systems to improve their collections by segmenting the delinquent customers, prioritising them and applying a host of treatments. Whilst segmentation takes you so far, there are a host of other mathematical models that can be explored to improve what we call the “Collections Cascade”. Improvement in any step of the cascade can help improve the collections yield and there are a number of models that can be used. A few of them are listed below:
Bringing automation into the credit assessment process through credit scoring brings about significant benefits. Some of these benefits include: