Reinforcing the Insurance Value Chain
By Mark Anquillare
Sometimes the chains that bind can become the chains that unwind.
In a landmark study of how businesses create value, Michael E. Porter, an economist and professor at Harvard Business School, theorized about a series of distinct actions that help create value in products and services—in Porter’s language, a “value chain.” In making an automobile, for example, the chain begins with raw steel, glass, and rubber and ends with a shining product ready for the road. Materials are transformed and assembled during a series of steps into something progressively more valuable, with an end product greater than the sum of its parts.
Automakers and other manufacturers are also part of a different kind of chain—the global supply chain that connects service providers, retailers, and manufacturers to clients and consumers, a chain in which the insurance industry doesn’t stand alone but as an indispensable supporting link. In reality, insurers are a critical factor at every point along the chain, providing confidence in case of disruption and counsel to bring solutions when a link suddenly fails, leading to costly interruptions in the wake of fraud, theft, or natural disasters.
Closer study of the insurance link in the broader chain offers further insights. Just as in the business chain, fundamental elements of insurance also link together and are known collectively as the insurance value chain. In this series of links, retention of policyholders, precise segmentation, and efficient payment of claims all play contributing parts.
What’s at Risk?
Insurers want to understand what they write, where they should be writing, and how best to approach an emerging market. Initial product development and pricing are followed by underwriting. Down the chain from underwriting come policy processing and claims management. By the final link, which is policyholder satisfaction, the insurance value chain’s performance has been stress-tested for strength and effectiveness by two broad questions: How does an insurer decide which insurance lines and segments to serve? And how can risk be better understood?
The answers are rarely obvious, but ever-deeper pools of data are introducing a golden opportunity for growth and profit. For instance, in the homeowners insurance market, the relatively recent adoption of rating by peril has allowed insurers to reduce loss ratios, increase market share, and better define and price risks. Insurers are now able to set ratings for distinct risks, from wildfires and terrorism to floods and hurricanes. This is an example of the power of advanced analytics coming to bear on enormous data sets to yield path-breaking insights about levels of risk.
Many insurers use catastrophe models—the fruit of advanced analytics—in pricing, risk selection and underwriting, loss mitigation, reinsurance decision making, and portfolio management. Model output provides information about the potential for large losses before they occur, so both insurers and policyholders can prepare for, and potentially limit, the financial consequences.
Navigating Seas of Data
Similar advantages can be realized in handling claims. Insurers typically look to speed payment of meritorious claims and take a closer look at claims that seem questionable. The challenge lies in deciding how to navigate to the best outcomes across choppy seas of data. Once again, innovations come through advanced analytics.
In the age of analytics, tools have become available that can help detect and prevent fraud before an insurer even writes a policy. These models can pick up rating errors that often result in lost premium and can flag applications that carry a high probability of future claims fraud. It’s then possible to reduce the quoting process from minutes to seconds, allowing insurers to prepare new business estimates with greater speed and accuracy.
In responding to property claims, what’s the quickest way to assess damages? With new geospatial technology that has recently come online, insurers can in some cases visually inspect a property without dispatching an inspector to the claims site. Analytics are bringing fresh perspectives about risks relative to hazards. This view is valuable both for claims and in identifying vulnerabilities before a loss occurs. And aerial images are changing the way claims are adjudicated and risks are underwritten in the United States.
Questions about Commercial Property?
In terms of commercial property, companies holding policies have long wondered about how improvements might affect their premiums. Tools are becoming available that enable owners to pose “what if” questions that could lead to a safer facility and lower loss costs, as well as significant reductions in premiums. What if a factory’s sprinkler system were updated? Cutting-edge advances in analytics are providing quick and accurate responses to these “what if” lines of commercial questions.
Among the benefits will be greater and finer resolution for commercial and personal lines as the power of data and analytics progresses to the point of sale. Even in terms of an individual auto or homeowners policy, consumers will likely be able to secure a quote with greater speed and less hassle than in many past transactions. So, there’s great potential for the consumer in the form of a better shopping experience and a more accurate price for insurance.
Some insurers identify and price risk more effectively than their competitors—and because of that often achieve better financial and risk management results. One fundamental tenet of risk management is that accurate rating information sits at the center of the underwriting process. Misreported information tends to lead to severely adverse consequences when actuaries use incorrect data to develop rating plans. The inevitable result is weak differentiation of risks and potentially costly risk management failures. Clearly, inaccurate data can contribute to bad pricing decisions. That, in turn, can lead to inadequate risk differentiation and poor risk management.
The ultimate link in the insurance value chain—reinsurance and the large-scale transfer of insurers’ risk—can reveal the true value of precise data and analytics. Insurers facing claims that often result from hurricanes, wildfires, hail, storms, and even terrorism call on reinsurers to handle some of the potential risks to policyholders and property. To prepare for high-stakes situations that follow catastrophes, both insurers and reinsurers can leverage software and reliable models to help them determine risk exposure and a fair price. This is risk management at its highest level.
Choosing a Long-Term Strategy
To help achieve superior financial performance, insurers need to adopt a risk management strategy that looks at the connections among all aspects of their business. Key requirements are recognition of all risk factors and accurate data collection through an ongoing rating integrity process. Accurate data can support better actuarial and risk analysis, better risk differentiation, better pricing, and better retention and risk management strategies.
Short-term strategies that look only at the immediate problem—whether a focus on retention to the exclusion of other fundamentals or saving money by cutting costs on programs that manage data integrity—are really just short-sighted. Especially with the escalating emphasis on enterprise risk management, insurers should consider a longer-term horizon.
That’s easier said than done, given the typical compensation structures that exist today. Many insurance companies provide their executives with incentives that emphasize certain ratios over others. For example, companies may narrow their focus to the underwriting expense ratio or retention ratio and lose sight of overall profitability. With the advanced analytic and underwriting tools available today, it’s possible for any insurer to manage appropriate retention and strong underwriting goals—and potentially improve financial performance.
In the insurance value chain, growth of market share tends to be the biggest challenge. Finding an analytic solution that makes premiums better reflect the risk in a company’s current book is just the first step. Carriers also need to be able to price market segments they’re not currently reaching—geography and classes of risk—to achieve competitive rates and maintain rate adequacy. The biggest obstacle is often the lack of credible internal data in these segments. Therefore, many insurers need an analytic solution that supplements their lack of credible data or the lack of data entirely.
Analytics’ Role in the Value Chain
Analytics have added precision to actuarial work, which in turn has contributed to more accurate pricing overall and by individual risk. Predictive analytics has made the actuary’s job a little easier, in that analytics can help the actuary evaluate extremely large amounts of data more quickly and perhaps incorporate elements that previously weren’t available or considered relevant in the assessment of loss costs. As departments of insurance become more familiar with predictive analytics, these tools could help streamline the approval process. All told, advancements in predictive analytics could mean instantly leveling the playing field—simply by adopting an off-the-shelf analytic solution.
Recently, a number of insurers have begun to look into price optimization over a multiyear time horizon. Critical to the success of this technique will be the validation of actual realized results versus expectations. That information can then be used to further refine the price optimization model.
Where could the industry extend the frontier? The next big thing in pricing will likely be the complete risk assessment of an individual. In the same vein, it would be a policy applied across various insurance products—both personal and commercial lines. For example, vehicle-related information about a policyholder might be predictive for homeowners insurance, or the reverse case.
To reach this level of sophistication, an insurer will likely need to merge data for auto, homeowners, and other lines of business. The insurer may have to prepare and run predictive models to determine the complete risk assessment for either an individual risk or a policy across all lines. This idea is still in its infancy but will likely bring powerful change to the industry when more fully developed—and further strengthen the insurance value chain.