In insurtech as in other fields, implementations are often complex. It’s not unusual for problems to arise before a new solution is fully deployed, integrated with external technologies, and delivering robust business value.
DataCubes is in the midst of rapid growth, not only in the number of employees and customers but also in the maturity of our organization. One sign of the increasing maturity is the value placed in the creation of an established People Development function. I joined DataCubes two months ago as our first-ever Director of HR, with a mission to help manage and optimize our ongoing growth.
Optical character recognition (OCR) is a decades-old technology that converts scanned images to text, and it has played an important role across many industries throughout the years. But when it comes to digitizing applications for commercial insurance underwriting, OCR is just the first step of the innovative process. Having worked extensively with the improvement of the insurance application process, I would like to give some thoughts on ways to improve underwriting productivity. Artificial Intelligence (AI), and specifically machine learning, can take the intake process well beyond OCR, making it possible to capture data accurately from scanned documents, derive meaning from that data, and apply it in ways that make the underwriter’s job easier and more profitable.
Last week’s blog took a look back at DataCubes’ 2019 highlights. In this blog, I’d like to focus on where the industry stands today and what’s ahead for insurtech and AI in 2020.
2019 was a pivotal year at DataCubes—one that saw our partners make great progress in transforming their underwriting businesses. The year also saw DataCubes continue our exponential growth while taking important steps to create further success in 2020.
Profitably underwriting commercial auto is a critical problem area for most insurers. The National Association of Insurance Commissioners reports that commercial auto losses have risen for the past four years, despite rate increases every quarter since 2011. Commercial auto rates rose an estimated 6 to 12 percent in 2019, according to the risk management firm Willis Towers Watson.
DataCubes’ rapid growth shows there’s a real hunger for insurtech solutions that use sophisticated analytics to streamline and automate commercial underwriting. But breakthroughs in this field don’t come only from innovative startups like DataCubes. Academic institutions play an important role in the insurtech ecosystem. They introduce the next generation of insurance and insurtech leaders to recent machine learning methods and technologies, and expose them to current industry issues. They research and report on novel approaches to solving complex industry problems. They foster interdisciplinary collaboration.
DataCubes recently migrated to a modern microservices architecture, using Docker-based containerization. In doing this, we’ve joined a large number of tech giants, traditional companies and fast-growing startups who have gone public about their move to microservices architecture. These companies range from Airbnb, Amazon and Comcast, to Netflix, Uber and Walmart.
I’ve always been an entrepreneur. Either consciously, or just by habit, I have always looked for more advanced solutions to solve problems. From when I was living on a farm as a boy to this very day, finding innovative ways to add value has always been front and center for me.
Being a 10-year veteran of the insurance and financial services industries, I have seen hundreds of companies execute successful digital transformation initiatives, and others fail. After careful observation, I have discovered what characterizes a winner. Here are some of the best practices on how any carrier can help build insurtech success.