Business intelligence and machine learning highlight the need for change

The rate at which technology is transforming the P&C insurance industry is nothing short of amazing. Innovations are coming to market almost daily from InsurTech startups and established vendors — both within and outside of 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 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 (BI) 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 BI solutions from scratch.
2. Partner with a specialized BI 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 new core systems in their efforts to modernize, and multiple vendors offer business intelligence and/or data analytics systems as add-ons.

Related:  How the insurance industry is embracing advanced analytics

Adopting advanced analytics

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 focuses primarily on two opposing facets of using such analytics — competitive advantages and barriers to success. What I find amazing is that successes and barriers are based on the same thing — the data. Successful companies have their data house in order, leaving behind those who are scrambling to find a solution to keep them in the game.

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.

Many machine-learning systems provide insight to a human, who can then act on those insights. Examples of machine learning processes with the potential to become fodder for early adopters include:

• Scoring risks based on characteristics of the policy and the insured; the application can learn from actual outcomes and adjust as necessary.
• Recommending staffing needs when a catastrophe strikes.
• Determining reserve opening amounts.

These types of applications can utilize data that originates within transaction systems as well as external data that can be purchased and appended to transaction data. Data solutions within PAS vendor offerings need to be powerful and capable enough to ensure that business people will trust the output of analytical applications that use their data. Once they trust the data, PAS vendors are back to options very similar to what I described years ago for integrating business intelligence applications:

• Build their own advanced analytics/machine learning solutions.
• Acquire an advanced analytics/machine learning vendor.
• Partner with a vendor to provide predictive analytics and machine learning models.

While the variety of recent technological advancements has indeed been remarkable, one only has to look at the constants facing insurers who want to act fast to understand what will make or break the carriers of the future. To prepare for future technologies and take on transformative change with speed, P&C insurers must ensure that their core systems are built on platforms designed for openness and architected for change, and that their data is in a controlled, well-managed state.

As more carriers complete core system modernizations, then move into and beyond managing data assets, vendors will begin to focus on configurable AI and machine learning solutions to tackle the next wave of the big data revolution.

Tomorrow’s successful carriers will be the ones planning for their futures today — the ones implementing core software platforms designed to accommodate unlimited vendor integrations, augmenting those core systems with solutions that provide strong data management and control capabilities, and building with an eye to a future defined by collaboration, innovation and change.

Aviva Phillips is the director of Solution Consulting at Duck Creek Technologies. Contact her at aviva.phillips@duckcreek.com.