Business Intelligence, Machine Learning, and the One True Constant: Change
The rate at which technology is transforming the P&C insurance industry is nothing short of amazing. Seemingly every day, innovations are coming to market from insurtech startups and established vendors – both within and outside the insurance industry. When one of these innovations is broad and powerful enough to provide a transformative effect on anyone who sees its value and adopts it, insurers often find themselves looking at a familiar set of options for how to improve their operations through integration of these new technologies.
Seven years ago, I wrote an article that included the following passage:
“Business intelligence can add significant additional revenue, as well as make carriers more loyal to their policy administration systems (PAS) vendors. When it comes to making this wealth of data available, and providing easy-to-use analytical capabilities, the options for PAS vendors are:
1. Build their own business intelligence solutions from scratch
2. Partner with a specialized business intelligence vendor to provide an easy interface between transaction data and BI tools
3. Acquire a BI vendor outright and integrate their technologies with the PAS vendor’s platform
4. Partner with a BI vendor to build an analytics and reporting solution, and offer it as an add-on to their core PAS system.”
Over the past seven years, each of these scenarios has played out in the market. Many insurers have implemented – or are now implementing – new core systems in their efforts to modernize, and many of the vendors of those core systems offer business intelligence and / or data analytics systems as add-ons.
More recently, I have been reading about the adoption of advanced analytics, including machine learning, and the integration of analytics output into business transactions. The news is primarily focused on two opposing facets of using such analytics – competitive advantages that are opening up, and barriers to success. The thing that I find amazing is that both the successes and the barriers are based on the same thing – the data. Companies that are succeeding have their data house in order, while those being left behind are scrambling to find a solution that will keep them in the game.
So, what will PAS vendors do as more companies modernize their transaction capabilities, but still struggle with data and analytics? They will need to demonstrate that they can provide their customers with machine learning applications tied to their data and transaction solutions.
