Tag Archives: satellite imagery

How to Use All the New Data

Most people who purchase an insurance policy are faced with the daunting task of filling out an extensive application. The insurance company – either directly or through an intermediary – asks a myriad of questions about the “risk” for which insurance is being sought. The data requested includes information about the entity seeking to purchase insurance, the nature of the risk, prior loss experience and the amount of coverage requested. Insurers may supplement that information with a limited amount of external data such as motor vehicle records and credit scores. The majority of information used to inform the valuation process, however, has been provided by the applicant. This approach is much like turning off your satellite and data-driven GPS navigation system to ask a local for directions.

According to the EMC Digital Universe with research and analysis by IDC in 2014, the digital universe is “doubling in size every two years, and by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes.” That explosion in the information ecosystem expands the data potentially available to insurers and the value they can provide to their clients. But it requires new analytical tools and approaches to unlock the value. The resulting benefits can be grouped generally into two categories:

  • Providing Risk Insights: Mining a wider variety of data sources yields valuable risk insights more quickly
  • Improving Customer Experience: Improving the origination policy service and claims processes through technology enhances client satisfaction

For each of these areas, I’ll highlight a vision for a better client value proposition, identify some of the foundational work that is used to deliver that value and flesh out some of the tools needed to realize this potential.

Risk Insights
Insurance professionals have expertise that gives them insight into the core drivers of risk. From there, they have the opportunity to identify existing data that will help them understand the evolving risk landscape or identify data that could be captured with today’s technology. One can see the potential value of coupling an insurer’s own data with that from various currently available sources:

  • Research findings from universities are almost universally available digitally, and these can provide deep insights into risk.
  • Publicly available data on marine vessel position can be used to provide valuable insights to shippers regarding potentially hazardous routes and ports, from both a hull and cargo perspective.
  • Satellite imagery can be used to assess everything from damage after a storm to proximity of other structures to the ground water levels, providing a wealth of insights into risk.

The list of potential sources is impressive, limited in some sense only by our imagination.

When using the broad digital landscape to understand risk — say, exposure to a potentially harmful chemical — we know that two important aspects to consider are scientific evidence and the legal landscape. Historically, insurers would have relied on expert judgment to assess these risks, but in a world where court proceedings and academic literature are both digitized, we can do better, using analytical approaches that move beyond those generally employed.

Praedicat is a company doing pioneering work in this field that is deriving deep insights by systematically and electronically evaluating evidence from various sources. According to the CEO Dr. Robert Reville, “Our success did not come solely from our ability to mine data bases and create meta data, which many companies today can do. While that work was complex, given the myriad of text-based data sources, others could have done that work. What we do that is unique is overlay an underlying model of the evolution of science, the legal process and the dynamics of litigation that we created from the domain expertise of our experts to provide context that allows us to create useful information from that data built to convert the metadata into quantitative risk metrics ready to guide decisions.”

The key point is that if the insurance industry wants to generate insights of value to clients, identifying or creating valuable data sources is necessary, but making sense of it all requires a mental model to provide relevance to the data. The work of Praedicat, and others like it, should not stop on the underwriter’s desktop. One underexploited value of the insurance industry is to provide insights into risk that gives clients the ability to fundamentally change their own destiny. Accordingly, advances in analytics enable a deeper value proposition for those insurers willing to take the leap.

Customer Experience
Requiring clients to provide copious amounts of application data in this information age is unnecessary and burdensome. I contrast the experience of many insurance purchasers with my own experience as a credit card customer. I, like thousands of other consumers, routinely receive “preapproved” offers in the mail from credit card companies soliciting my business. However appealing it may be to interpret this phenomenon as a benevolent gesture of trust, I know I have found myself on the receiving end of a lending process whereby banks efficiently employ available data ecosystems to gather insights that allow the assessment of risk without ever needing to ask me a single question before extending an offer. I contrast this with my experience as an insurance purchaser, where I fill out lengthy applications, providing information that could be gained from readily available government data, satellite imagery or a litany of other sources.

Imagine a time when much of the insurance buying process is inverted, beginning with an offer for coverage, rather than a lengthy application and quote request. In that future, an insurer provides both an assessment of the risks faced, mitigations that could be undertaken (and the savings associated), along with the price it would charge.

While no doubt more client-friendly, is such a structure possible? As Louis Bode, former senior enterprise architect and solution architect manager at Great American Insurance group and current CSO of a new startup in stealth-mode observes, “The insurance industry will be challenged to assimilate and digest the fire hose of big data needed to achieve ease of use and more powerful data analytics.”

According to Bode, “Two elements that will be most important for us as an industry will be to 1) ensure our data is good through a process of dynamic data scoring; and 2) utilize algorithmic risk determination to break down the large amounts of data into meaningful granular risk indexes.” Bode predicts “a future where insurers will be able to underwrite policies more easily, more quickly and with less human touch than ever imagined.”

The potential to use a broader array of data sources to improve customer experience extends well beyond the origination process. Imagine crowdsourcing in real time the analysis of images to an area affected by a natural disaster, getting real time insights into where to send adjusters before a claim is submitted. Tomnod is already crowdsourcing the kinds of analysis that would make this possible. Or imagine being able to settle an automobile claim by simply snapping a picture and getting an estimate in real time. Tractable is already enabling that enhanced level of customer experience.

The future for insurance clients is bright. Data and analytics will enable insurers to deliver more value to clients, not for additional fees, but as a fundamental part of the value they provide. Clients can, and should, demand more from their insurance experience. Current players will deliver or be replaced by those who can.

I’d like to finish with a brief, three-question poll to see how well readers think the industry is performing in its delivery of value through data and analytics to clients. Here is my google forms survey.

How to Avoid Pitfalls in Insurance Innovation

The words “disruption” and “innovation” are in everyday lexicon surrounding many concepts, products and services. At times, it seems almost impossible to navigate the full range of opportunities for insurance innovation. This makes it extremely difficult to make the right choice to adopt a specific technology or strategy to redefine or reinvent a business.

Certainly, budgets are not limitless, and time is scarce. How do we ensure that we invest in the right technologies at the right time and prioritize the investments in proper order? How do we make sure that the opportunity to adopt a new technology is not being overlooked or unintentionally delayed?

Innovation Teams

Many insurance carriers deployed innovation teams to stay on top of the technological landscape and drive forward-thinking decisions. These teams have done a marvelous job.

Yet, even with these teams in place, most organizations seem to drastically fall behind in adopting the technology early enough to make the most impact. With modern, cloud-based SaaS offerings that can be fielded without internal IT investments, with very little set-up requirements and with lean operations provided by young ventures that drive most of the innovative technologies to the front lines, why do we still find it difficult change?

In a recent article, Steve Blank, a serial entrepreneur recognized for customer development methodology that led to the Lean Startup movement, described two of the most common issues with deploying innovations teams to drive organizational change. The first: making it easy for innovation teams to drive the selection of the right business units to field the solutions as soon as possible. The second: ensuring that the organization separates the execution part of the business, which operates an existing business model, from the innovation business unit, which is modifying the existing model or creating one.

Beyond the Innovation Noise

The key to a successful continuous innovation cycle is looking beyond the hype and the related group think about innovations.

Technologies such as big data, analytics and Drones receive a lot of attention. However, getting full value from them is far from simple.

Big data, for example, interprets information with analytics tools. To derive value from it, however, it is important to identify what purpose is to be achieved, what data is important and where to acquire it — before using the analytics. Experts say the most critical, time-consuming and expensive part of adopting big data comes from the effort required to analyze the business and all of the data sources, so the upfront investment is quite high.

The spotlight on drones often seemingly ignores the limitations of the technology. In certain weather conditions, like wind, rain and fog, the control of the drone becomes challenging, and the video quality drops. In addition, use of drones is highly unscalable, as one operator can only control a single drone within the line of sight.

In addition, satellite imagery can be significantly more effective in collecting real-time aerial imagery of an area hit by a storm, if visibility allows. This is a possible threat as real-time satellite technology becomes more affordable to the masses.

Is there a future in drones? Absolutely, but it will take time to perfect this technology, as the industry is still exploring the right fit in the field. This is where looking outside the box provides the clues that prevent falling into a common innovation trap.

Think Outside the Box, Think ROI

Sometimes, looking too closely at a solution creates a commitment to a technology that has a much longer innovation and implementation cycle than expected. Playing with new technology is always fun, and there is value in being recognized as the first to explore new tools for the organization. However, the goal has to be generating a competitive advantage that provides the highest benefits – the best ROI.

Today’s most important technologies are the ones that can be implemented with very low up-front investments in IT support and employee training and the ones that can simplify or even eliminate the largest, most unscalable and expensive operations.

Technologies that deliver enhancements to existing business processes like mobile tools, real-time video communications, litigation document management solutions and field resource planning and dispatch platforms are easier to acquire and evaluate. These technologies are less expensive and cause less conflict with an existing part of the business. At the same time, they deliver substantial tactical improvements in operations and can be quickly deployed within the necessary workflow.

Larger-scope solutions such as claims management, policy management and billing systems typically require a significant modification or a complete replacement of existing systems. Implementation or upgrade of these systems is a high-risk exercise, while the projected ROI is mostly strategic — long-term efficiency, productivity and other future capabilities.

To assess the value of investment in a specific technology, most enterprises have adopted the Lean Startup model, piloting software before full adoption. There is, however, a significant difference between a proof-of-concept and a proof-of-value approach to identifying the right technologies. Proof of concept starts at the business problem and validates a solution using specific technologies, while proof of value begins by looking for a specific solution to a known business problem. The first validates that the technology works; the latter ensures the investment is worthwhile.

For any organization looking to continuously change and innovate, the right approach is in proof of value – being able to quickly assess and adopt solutions with the lowest barriers, fastest implementation and highest returns.