My founder’s story begins at an insurance carrier in South Africa. After extensive involvement in the pricing team, I noticed that the industry standard for building insurance models was a very archaic process. It took us several months to deploy a fresh set of models. I knew that Machine Learning (not rebranded statistics) had been transformative in many other sectors. It baffled my mind to see that this was not happening within the insurance industry. I learned that the biggest barrier to integrating Machine Learning into insurance pricing was the black-box nature of the model output. In insurance pricing, we need explainable and transparent models and the black-box nature made it difficult for pricing experts to understand and justify why the model output was the way it was.

Even after leaving my position in the team, this problem stayed with me. I couldn’t let it go. I wrestled with it for a long time, searching for a solution. Despite all the hype, the market leaders were still using modeling techniques developed in the 1990s. It became apparent to me that change wasn’t going to come from the established players. If I wanted to see real progress, I would have to build it myself.

In order to revolutionise the industry, we needed to rework the process from the ground up. As a result, the core of our product is a transparent Machine-Learning algorithm that delivers state-of-the-art performance. After developing a minimum version of the product, I had the opportunity of testing my models against competitors in the market. The results exceeded expectations giving me the confidence I needed to raise funds and build a fully-fledged product.

Fast forward one year to where we now have a stable version of our platform and have already onboarded our first paying users. The discussions we’ve had with various pricing teams have been very insightful in understanding the problems they face with their current rate-making approaches. It has been bizarre to see how fragmented their pricing processes are. Instead of addressing their technical debt, they hire more employees which just leads to more fragmented and inefficient processes. Some pricing teams at the larger carriers have grown from 10 people in 2010 to over 100 in 2020. Collaboration and productivity do not scale linearly with headcount and are falling short of management’s expectations.

We are setting out to build the best-in-class pricing platform. Our solution injects game-changing speeds into the pricing process. It delivers state-of-the-art performance with full transparency and explainability. As part of this vision, we want to streamline the modeling process to empower pricing teams to do their jobs better and faster. We are actively looking for customers who would want to embark on this journey with us. If our solution resonates with you, feel free to reach out.