Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
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:
This blog was originally published on 13 March 2019 and updated on 3 April 2019.
For a while, we have been running a blog series on cognitive biases and logical fallacies that data scientists should avoid. In this final blog on the subject, we look at some of the other logical fallacies and how they might crop up in data analytics.