Modern technology, along with industry knowledge and experience, means an analytics project can be delivered better, faster, stronger and cheaper.
Part of a blog series.
As we noted in the first post in this series, the Insurance industry has some catching up to do when it comes to implementing modern analytics platforms.
You may remember the heady days of a decade ago when “analytics” and “big data” were not only the most used words at every conference you attended but even had national advertising campaigns devoted to them. It seemed that everyone in the business world was focused on analytics already by that time. Well, almost everyone. Those early analytics projects were difficult and very expensive, as cutting-edge technologies often are. The Property & Casualty Insurance industry found itself at a crossroads: Pursue one of these exciting (if costly) projects, or focus on modernizing core business functions? While there were certainly many P&C Insurance companies that were early adopters of analytics technologies, the priority of the industry as a whole was on those functions that, “keep the lights on.” Many of the people we have spoken with about those early insurance analytics projects don’t feel like they got the most out of their investment. But the field of analytics has continued to evolve to the point that now is a great time to change the focus from platform modernization to analytics maturity.A Distribution Manager’s Dream
Imagine you are a distribution manager for a mid-sized Property & Casualty Insurance firm. You have focused for the last few years on modernizing your platforms and ensuring you have maximum exposure on all the channels you find relevant. You have worked with marketing to ensure your social media presence is dynamic and engaging. You have worked with IT to create and maintain your online and mobile quoting portals. You work with actuarial to make sure your rates are appropriately competitive. But you still feel there’s something more you can do to get an edge. There’s a data warehouse that you maintain for monthly reports, but is there something more you should be doing with all this data you have to take your business to new heights? You tried an analytics project a few years ago after learning about a success story at a conference, but it did not yield the results you (or your management) desired. You wonder: What is the state of the art now? Can we find new insights with new technology?Better, Stronger, Faster
Even today, it is possible to find source systems in the insurance industry, among others, that discard transactions on a monthly or otherwise regular basis. These legacy systems are maintained to run off business that was established in those systems. Those among us who have worked in this industry for some time can remember working on projects to capture that data regularly and move it to a more permanent store for further operations and reporting. As insurance analytics platforms modernized, our ability to capture real-time data improved. We went from easily automated monthly snapshots to daily overnight snapshots, and then to real-time replication, where every transaction in a source system at any time of day can be instantly replicated to a database in any location we desire for whatever purpose we need. Even better: This data can be captured regardless of the system in which it resides. The modern enterprise relies on several systems to capture and maintain data. Current technology allows for the real-time capture of this data regardless of the source system. Mainframe, VSAM, DB2 and other older, but still used, data structures are all on the table for modern analytics projects. The current state of affairs allows for true and truly painless change data capture (CDC). For the Distribution Manager mentioned above, this is fantastic news. Each day, the Distribution Manager’s company quotes hundreds or thousands of policies. Whether they are working with an Agent or automated underwriting through an online portal, each potential customer may go through several iterations of quotes before finally deciding on a coverage level and policy, or ultimately walking away. What if we could have access to all that data, replicating it in real time for analysis later? If we had all the versions of all the quotes, we could understand, in real-time:- How market preferences are changing
- Who abandoned quotes?
- Why do people abandon quotes?
- How premium is performing against claims, in-depth
- Are the people taking our policies riskier than we would like?
- Are the people walking away from the kind of customers we really want to have?
- How should we change our underwriting guidelines to remain competitive?
