The buzz about Big Data began several years ago in the insurance industry. Innovative start-up companies like Google, Yahoo, and Facebook used some of the first Big Data solutions since their business model was and is dealing with the aggregation, organization, and actionable usage of vast amounts of data in near real time. That has led to the rapid development of Big Data related technologies, tools, and techniques that the insurance industry is just beginning to avail itself. Further, those technologies, tools, and techniques have matured to the point where the insurance industry – P&C, Life, and Health – can and should take better advantage of the various Big Data solutions at their disposal.
For the insurance industry, Big Data should mean that the technologies and capabilities that are now part of the data and information ecosystem begin to become pervasive inside insurance companies. For example, the insurance industry’s main concentration with Big Data to date has been focused on the obvious (and needy) areas like policy administration and claims adjudication. The use of advanced data technologies in these areas has significantly improved many insurer’s abilities to provide improved customer service and responsiveness, detect fraudulent activity more quickly and create data stores of information that is better organized, less duplicative, and more actionable. However, the insurance industry is still leaving plenty of operational efficiencies and effectiveness untapped. Big Data is about extending those initial data forays into the industry to the rest of an insurance organization.
Insurers need to remove their technology blinders and look beyond what is often viewed as traditional technology limitations. The insurance industry is rife with self-inflicted limitations that have created unnecessary business process steps and workarounds. More specifically, P&C insurance companies could be doing more with advanced analytics to analyze past policy and claim data to generate new rate structures. Yes, some P&C insurers are doing this, but many more have yet to advance their Big Data efforts to this point. P&C insurers could and should be using nationally available data like weather patterns for predictive analytics to reduce storm damage claims or extensive geocoding for commercial markets that would allow custom rating of policies.
So what else is holding the insurance industry back? Well, all of the usual things like resources, talent, budgets, legacy platforms, priorities, operational constraints, and the list goes on. It’s also the case that the industry – particularly P&C insurers – has “outsourced” a lot of advanced data and information innovation to the Insurtech ecosystem. That’s fine, but the key will be leveraging the technology and process innovations offered by Insurtech startups into something that carriers can sustain, scale, and integrate into their operational DNA. Taken as a whole, it’s fair to say that the insurance industry has approached Big Data – inclusive of everything under that tent – in fits and starts. The good news is that over the past several years insurers have slowly but steadily been building their data warehouses, implementing advanced analytics processes and toolsets, improving their self-service reporting capabilities, and generally gaining the kind of data-leveraging knowledge that only comes with some time and experience.
So insurance companies do not have to invest tons of money to start their journey. With the advent of cloud offerings, companies can easily setup Big Data infrastructures and begin to reap the benefits with as little as one or two use cases. The most significant investment is finding the right talent – talent that can not only help with finding a correct use case to start – but can also set up the machine learning algorithms and finalize an artificial intelligence (AI) solution for that use case. Once the business sees the benefit of using a next-gen Big Data solution approach, the floodgates will open with more solvable problems that have been ignored or handled with elaborate process workarounds.
For example, a simple use case for auto insurers would be to give discounts for lower car mileages. One way to implement this might be to have insureds upload a picture of their car odometer at the time of renewal. The odometer image could easily be converted into a premium ratings algorithm with the use of machine learning and AI. This would allow insurers to provide competitive premium rates on an automated basis, providing financial and customer service benefits to insureds and customer retention value to the insurer.
For the insurance industry writ large, the time to intensify its overall investments of time and money into information and analytics is now. In 2019 and beyond Big Data is all about actionable information that enables proactive decision-making and operational awareness. That should lead the industry to better customer relationships along with more profitable business—that’s not a bad world to live in.
Originally published in
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