Insurers must keep pace with the constantly evolving risk environment. That means keeping pace with climate change and growing catastrophic event losses; the explosive growth of intangible assets and the challenge of valuing those assets; and the increasing exposure of their portfolio to cyber risks.
Despite changing and challenging conditions, some carriers thrive and prosper – while others stagnate. Almost any seasoned insurance executive can tell you a story or two about how a once-strong carrier devolved into obsolescence. The sum of the story is typically that those that wish to prosper must find new opportunities in an evolving environment – and new ways to innovate.
The idea that innovators thrive is based on more than just anecdotal evidence. There are detailed analyses that show how and why innovators thrive in the insurance industry.
According to one of these, a recent study by the consulting firm McKinsey, “insurance market shapers,” those that boldly innovate, create significantly more economic value than their peers. According to the report, on average, insurance “market shapers,” innovators in the top 20% of the market, create profits up to 20 times the industry average.
How are these innovators making their mark today? There are certain characteristics, but one area is abundantly clear: Companies winning the competitive race use advanced data and analytics to select, underwrite and price risks.
Historically, carriers have achieved profitability by ensuring their fundamentals were solid, by running an efficient operation, by tightly managing their portfolios and by mitigating portfolio risks. However, a whole new frontier for competitive differentiation is opening up as a result of the evolution of analytics, data and risk models. In fact, data and analytics likely will be the key to an insurer’s success in the future.
Today, however, the vast majority of insurers invest their resources and time managing the technical issues related to data and analytics: (1) how to tap into the needed data, (2) how to build risk models and (3) how to integrate analytics and models into workflows. Too often, they sacrifice a focus on the more strategic aspects: determining where to apply analytics in their business – and how to differentiate their data mix and models from their competitors to create strategic advantage.
Best-in-class insurers tend to master three key aspects of data and analytics:
1. Data Strategy
First is a focus on data strategy, specifically data acquisition strategy. Insurers are having trouble keeping up with the rate at which data is growing. It is therefore essential that your organization determines how it will acquire, process and integrate data at the speed of business. Without knowing (a) what data you are focused on leveraging, (b) where you are getting that data and (c) how you will incorporate that information into your workflow or model, you are really playing catch-up.
Make sure you are prepared to integrate all types of new data and information: IoT data for residential properties from devices like Ring, Alexa and Google Home; telematics data from personal automobiles and commercial trucking; and IoT and systems data from commercial businesses.
Data availability will continue to evolve. New data will be brought to market when all kinds of legal, privacy and access issues are resolved. You will be better positioned for success if your organization and systems are prepared to handle this data and pivot.
Cloud-based APIs provide the capability to readily use data as it becomes available – to integrate that data as soon as it is available into core workflows.
See also: How to Unlock Data--and Profitability
2. Next-Generation Analytics and Risk Models
Most insurers are now commonly using analytics models in risk selection and pricing. There are varying degrees of proficiency and sophistication, but the insurance market has widely adopted these technologies.
Where insurers most often struggle with creating models is in finding and refreshing the right data for their model(s); regularly monitoring and adjusting existing models; innovating and testing new models for new business applications; and efficiently managing the entire process to free data scientists to focus on core/strategic priorities.
Most insurers are well aware of the problems in getting the right data into the models in a timely manner. But they are not aware that there are solutions and consultants available to solve this technical problem for them in an efficient and cost-effective manner. Nor are they highly focused on the other challenges of managing and improving their risk models on a regular basis – nor on creating new models in an agile and rapid way.
Being prolific in experimenting, testing and innovating with risk models across risk selection, underwriting, pricing and claims will eliminate significant pain points and present unimagined opportunities. In this day and age, your organization should have this as one of their primary business priorities.
3. Embedding Analytics into Workflows
The biggest point of failure for firms is in actually operationalizing risk models – in integrating analytics and risk models into workflows and processes across their insurance lifecycle.
If data, analytics and risk models are not innovated, adopted and put into the hands of those who need them when and where they need them – of what use are they?
Insurers are often proud of their risk models. The models may often be unique and a differentiator for their firm. However, most insurers today simply stall or do not integrate these models into workflows where they can be seen and used by underwriters and adjusters. Yet again, there are solutions and consultants that are adept at handling these technical details at a cost that is well worth the resulting innovation.
See also: How to Benefit From the Power of Data
Driving From Systems of Record to Systems of Insight
Insurers that want to own the future must understand the strategic importance of these critical steps. A smart approach is to focus on the strategic direction of your data and analytics program – and the strategic projects and models critical to that program – and outsource the technical challenges of acquiring and integrating data and analytics into workflows.
Modern analytics can help insurers more accurately price risk, capture and grow premium, optimize claims outcomes and enhance customer loyalty. Providing unique and embedded insights when and where they are needed empowers your organization to adapt to an ever-changing world.
