Predicting claims in health insurance – Part II

Insights from our health insurance POC

This is the second part of a two-part blog describing one of the POCs we’ve recently completed in Remitrix. In this POC we aimed to predict the number of claims per year in two health insurance products: Ambulatory insurance and Surgery insurance.

Predicting claims in health insurance – Part I

Insights from our health insurance POC

In this 2-part blog post we’ll try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. The first part includes a quick review the health Continue reading

Why we chose AWS and why our costumers are very happy with this decision

The main purpose of this blog is, as the name suggests, is to share the main advantages of using AWS in RemitRix, but before we start talking advantages, we should probably explain what is AWS.
Amazon Web Services is a cloud computing platform that offers a variety of services including Infrastructure as a Service (IaaS), Platforms as a service (PaaS) and Software as a service (SaaS). The great thing about AWS, other than the obvious fact that everything is cloud based and therefore can be used by anyone, anywhere in the world, is that it has many different Continue reading

A Bried History of ML

The term Machine Learning (ML) was first used by Arthur Samuel, one of the pioneers of Artificial Intelligence at IBM, in 1959. The name came from researchers who observed computers recognizing patterns and developed the theory that computers could learn without being programmed to perform specific tasks. They began exploring artificial intelligence to see how capable computers were of learning from data Continue reading

Improving lapse predictions (sigma 6/2017: LIFE IN-FORCE MANAGEMENT)

Data analytics – descriptive and predictive – can be used to improve retention of inforce business in several ways. Statistical models can be applied to investigate and better understand who lapses and why, using data systematically collected from different sources beyond traditional policyholder information. Based on the understanding of the underlying drivers, predictive models Continue reading
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