Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
For a while, we have been running a blog series on cognitive biases and logical fallacies that data scientists should avoid. In philosophy there are a host of informal logical fallacies – essentially errors in thinking – that crop up every day. In this series we have looked at the practice of data science to determine how these same fallacies also occur. Today we will be looking at fallacies and their manifestation in credit: The Monte-Carlo fallacy and the Hot-hand fallacy with some studies in the credit world.
Recently my team and I were sitting in a meeting with a potential client debating the basic functions of our originations software. To the business analysts who were leading the RFP process, the most critical feature seemed to be whether or not our solution would be able to offer web form fields that were customisable by the business user.
In an outbound sales environment, the agent needs to work through a long list of customers and the more information available to the agent on the customer, the better.
We apply the science of data analytics to assist our clients within various aspects of their customer-driven business and engagement process. Our products make use of predictive modelling techniques to facilitate the treatment of customers at the various stages of customer lifetime, for example during onboarding, growth and retention.
In a collections environment, an agent needs to follow up with numerous customers on their outstanding credit and the more distinct information the agent has on each customer, the better the agent will understand who they are interacting with and what the opportunities, risks and expectation of the collections call with the client are.