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Staying relevant has always been a top priority for insurers, especially so during uncertain economic times, and good data is always an important tool, says Scott G. Stephenson, president and chief operating officer of Verisk Analytics Inc., the parent company of Insurance Services Offices Inc. and several other organizations providing information to measure and manage risk. Mr. Stephenson offers suggestions to help insurers innovate, evolve and grow by using advanced analytics and risk-based decision-making.
It's no secret that navigating today's uncertain economic landscape has proved to be a significant challenge for insurance business leaders, and ever-changing market conditions have made the ability to stay relevant to customers difficult for even the largest insurers.
So how do insurers, both large and small, differentiate themselves among their markets and customers? How can insurers scale their operations, calculate multiple layers of business and technology strategy, and continue to grow in such harsh economic conditions?
The answer can be found in one word: innovation.
But simply being creative and inventive as a company is not enough. To keep its customers and survive, an organization must evolve intelligently as well as attract and retain talented staff. True innovation requires a deep understanding of markets, customers and cutting-edge technology, and the right people are needed to drive the overall business strategy. For the modern insurance enterprise, such innovation begins with a dedication to advanced analytics.
As with most businesses, sustained growth is the key to success in the insurance sector, and companies should focus on achieving organic expansion by leveraging advanced analytics and risk-based decision-making. For example, in the mid-1990s, personal auto insurers began exploring the use of predictive analytics to create better risk-based rating models. Those early adopters of analytics successfully used innovation to gain a key competitive advantage in attracting and retaining customers, thus growing their market share. But insurers that adhered to more traditional rating systems have diminished their prospects for growth, or even survival.
Achieving organic growth through analytical innovation requires three key elements:
• Executives who embrace competition by using analytics as sound business practice.
• Ever-widening exploration and exploitation of high-quality data resources.
• Emphasizing human capital to develop and execute their business models and strategy.
Even the most sophisticated and cutting-edge analytical tools must be supported by solid business knowledge and judgment. In fact, this is where analytical innovation begins.
Developing or exploring analytic solutions doesn't start as a formal statement of statistical prediction; rather, solutions grow from business leaders' experiences and knowledge of the market.
For example, in the case of personal auto insurance, underwriters used credit information long before the first insurance-specific credit models were developed. Analytics helped insurers better understand the relationship between credit histories and the cost insuring policyholders. By embracing those insights, successful insurers applied analytics to improve their understanding of risk and to create differentiated pricing for policyholders.
Retaining and attracting customers is another important facet of growth, typically dependent on solid management skills, reasoned insights and market awareness. Good leaders regularly review their customer portfolios and consider the implications of new products, expansion into new markets or the addition of alternative channels. Such considerations can be further enhanced with access to advanced data sources such as social networking, competitive intelligence, geocoding and more.
The bottom line is that management must increasingly innovate with fresh market data and advanced analytics to identify patterns that can signify growth opportunities, improved expense ratios and higher levels of customer and agent loyalty.
As technology and computing power have evolved over the past decade, so have systems to collect and manage vast amounts of data. For the modern insurance enterprise, the ability to collect and mine data is critical to shift from risk selection to risk management, and from adverse selection to pricing risk appropriately for long-term customer relationships. No one has all the data they need, but leading insurers continually look at multiple external data sources to better understand their portfolios and maintain a competitive advantage.
Furthermore, the availability and proactive collection of relevant data constitute the foundation for building effective predictive models. New sources of relevant data that insurers can use when developing innovative analytic models are increasingly available. For instance, personal auto insurers now leverage vehicle telematics data to gain new insights into the way people drive and determine how much they actually drive compared with an estimated mileage exposure base.
Large and small insurers alike must develop predictive analytic capabilities to remain competitive in today's insurance marketplace. But building and supporting the systems and processes that drive robust analytic solutions are often costly and pressure the budgets of even the largest insurers. To help overcome budget limitations, providers with strong analytic products and consulting services are emerging to cost-effectively supplement insurer skills. But no matter how companies choose to overcome data and resource constraints, executing on analytics decisively and creatively is critical to productive innovation and competitive differentiation.
Having access to relevant data is helpful only if it is properly analyzed, integrated and cultivated into actionable insights for smart decision-making. To that end, securing the right analytical talent can mean the difference between gaining an advantage and falling behind in an increasingly competitive insurance marketplace.
In reality, there often is not enough talent to serve the demanding, increasing appetite for analytic execution. If confronted with a dearth of talent, provider or consultant support to customize analytic solutions to understand risk can be an efficient, swift alternative to building in-house capabilities.
Actuaries' mathematical skills have made them particularly well-suited to training within the discipline of predictive analytics, and actuarial organizations worldwide have added predictive modeling to their curricula over the past several years.
In pursuit of productive innovation, insurers can encourage their actuarial teams to add predictive modeling to their skill sets. They also can recruit analytical professionals with experience in other industries.
Once constituted, the unique and persistent challenge of keeping the company's “analytic champions” content and engaged begins with ongoing management support for innovation. Also important is acknowledgement of day-to-day successes and marketplace accomplishments—as analytic experts help improve business results in key domains such as product pricing, underwriting, claims, customer retention, channel management and finance.
Predictive modeling is at the leading edge of business innovation, and insurers of all sizes should equip themselves with the science and skills necessary to build their analytic capabilities.
Today, innovation breeds new growth opportunities. Those insurers that can “invent the future”—by capturing information, analyzing it with specific goals in mind and implementing change quickly—are the ones poised not only to survive this volatile economic environment but are likely to emerge stronger and more competitive.
Scott G. Stephenson is president and chief operating officer of Jersey City, N.J.-based Verisk Analytics Inc., the parent company of Insurance Services Offices Inc. and other organizations providing information to measure and manage risk. Mr. Stephenson can be reached at firstname.lastname@example.org.