Two physicians walk into a hospital room. One is 63 years of age. She has 35 years of patient and illness experience and her diagnoses are consistently proven to be correct. The other doctor is a 26-year old intern. He went to an excellent school and is ready to dive into a lifetime practice.<\/p>\n
Which physician should the patient choose to direct care?<\/p>\n
Most of us would opt for the older physician because experience means something. If we did, however, we might be missing out on the latest advancements in care that are able to be applied by the young doctor. New knowledge may make all the difference in our quick recovery.<\/p>\n
This is very similar to the quandary of mitigating and pricing risk for insurers today. Insurers who have been experts at pricing risk for 50 years may be looking at new technology and data analysis, and wondering how it can place them in a better position than they are already in. Experience means something. But that would discount the game-changing role of new data streams and their ability to impact modeling and the understanding of risk.<\/p>\n
Insurers should probably choose to have both doctors in the room. Risk profiles may be more appropriate or precise if they are derived from a mix of long-term expertise and newly found data. They may be better understood if they use new technologies for analysis and visualization. The real question will be how to best balance and apply the mix.<\/p>\n
Insurers are adopting new hybrid data pictures right now. Some of them may understand they are shedding old philosophies and growing new ones, or they may just feel that they are responding to market trends. It is different within every organization and it certainly varies within insurance type. The end result, however, is going to be a revolutionary methodology for building and managing a portfolio of risk.<\/p>\n
To better understand the changes and how those changes will help insurers manage in an uncertain world, let’s look at how mitigating risk is evolving and how new pricing possibilities will help insurers adjust with greater frequency. We\u2019ll begin by looking at the specific factors that are changing the overall picture of risk.<\/p>\n
Insurers have always understood that we need to distinguish between good and bad risks. The game-changer is the new technology available to analyze the data, profile the risk and predict the risk you want. Using today’s modeling and predictive tools, insurers can get to the underwriting technical factors to select what they do want and deselect what they don’t want.<\/p>\n
Risk selection, however, is under more scrutiny now than it has been for some time. The greater our abilities become to predict and automate the selection of risk, the greater the potential for regulation that forces insurers to continue to underwrite substandard risks.<\/p>\n
Within some lines of business, however, technology will have the last say in selection, and the regulation will approve of it. Early in 2019, for example, New York State released guidelines for social media use that will allow insurers to scroll social media sites for relevant detail on insureds and applicants and set premiums accordingly.<\/p>\n
Once an organization understands its appetite for risks, it will be able to take advantage of large data to increase understanding of those risks. There are many new data streams from which to draw, such as telemetric data, data from wearables, connected home sensor data, and updated sources of risk data that would include GIS and medical information. As data types are changing, the granularity of the information that those streams provide is improving.<\/p>\n
Data enables modeling, but it also enables automation that can help insurers reach lower premium markets with higher transaction volumes without heavy manual touch. For example, the small-to-medium business market is today recognized as a market ripe for data-driven selection and automation. It is especially-beneficial because the pool of SMBs is growing rapidly and the kinds of SMBs that are growing the fastest are those that have vehicular risk, load risk or other very data-trackable risks. Using these data sources, insurers will be able to mitigate risk by building stronger risk portfolios at the same time they are expanding into growing markets and improving their competitive pricing.<\/p>\n
Pricing is an issue, however. Data’s availability is going to bring data symmetry to the insurance landscape. The opportunity that new data streams provide is excellent, but insurers need to remember that it is an opportunity being presented to all insurers, all startups, and all aggregators. In an uncertain landscape, it represents a competitive component that must be either be capitalized upon or acknowledged as a competitive threat that may harm us.<\/p>\n
Today’s tools are allowing for better risk selection. With better visualization, we are actually able to see the pools of policyholders that we want to attract.<\/p>\n
This is going to lead to “recruitment,” several steps deeper than target marketing. It used to be that personal lines insurers talking about target marketing would be considering a niche that contains adults of a certain age fitting a certain profile, filtered through common deselection criteria. Now, insurers will be aiming for a new marketing model\u2026where they understand the precise individuals, families and businesses they want, and they will simply go after those.<\/p>\n
The era of summarizing data and having the large book of business \u201cwork out\u201d against the law of averages, is over. Insurers want the detail and will use the detail to refine risk profiles. The competitive edge will be going to insurers who use the detail to underwrite better, balancing out many of the other increasing uncertainties.<\/p>\n
It will also lead to a better understanding of the insureds who are already under the umbrella. Mitigating risks within the current book of business is just as important as recruiting “good” risks. If an insurer can visualize their best 10,000 policyholders in a certain block of business, they may be able to work on helping their other policyholders emulate the 10,000 ideal policy holders through digital communications, automated warnings and further application of predictive analytics.<\/p>\n
If an insurer can identify the 5,000 clients with high risk tendencies, they can work toward limiting the damage that those risks can do. They can also, in some instances, raise their premiums. In fact, all of the changes we have discussed up to now have a bearing not only upon mitigating risk, but also upon improving pricing models.<\/p>\n
The uncertain world is going to push insurers to innovate with new product lines, meeting new demands and finding new markets. The ability to price accurately across new, innovative services, will bring additional stability to an insurer’s pool of risk.<\/p>\n
Because we have more data available about risk, we can exercise much more control over pricing. We can quantify risk differently, which allows us great flexibility. For example, we can quantify risk based on real-time data.<\/p>\n
This will allow us to accommodate (and price) usage-based insurance or on-demand insurance.<\/p>\n
Geographic data is now ubiquitous. Location-specific risk is being quantified and scored by data aggregators. Building construction materials are being cataloged. We\u2019ve discussed these new sources of data. But we can\u2019t neglect to understand that insurers are also starting to use sensors, drones, apps and GPS information to truly “learn” about risks. What we find out when we use these tools to gain a deeper understanding is going to help insurers to improve their pricing precision. In some cases, this means that an insurer could potentially gain margin while at the same time lowering pricing and mitigating risk. In some cases, it will mean raising prices, but either way, precision pricing will be more accurate and that will mitigate risk.<\/p>\n
In some segments, price margins will lower for competitive reasons, forcing insurers who aren’t prepared to compete without the same level of understanding. For example, insurance pricing aggregators (such as Insureon for business insurance), will force competitive pricing, so those who can’t price with precision will be at a tremendous loss when positioned in the same window next to those who have matured their pricing analytics.<\/p>\n
Motion metrics and telematics can give insurers responsive data streams. Insurers will no longer cross their fingers and hope for another good year, they will fluctuate pricing to match risk. Taking advantage of this requires organizational and system adjustments, but why wait? Tapping into the ability to fluctuate price will make the organization more competitive and prepared to handle new products that offer fluctuated pricing as a value-added differentiator.<\/p>\n
The world may be uncertain, but our data and our analytic tools are now able to paint clearer portraits. As risks grow and the world changes, our ability to know the risks in real time will improve. Those who prepare their data and analytics will understand their risk profiles, have clarity in their pricing structures, and will walk a lighted path of certainty and innovation.<\/p>\n","protected":false},"excerpt":{"rendered":"
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