Verisk Analytics: Evolution of an Innovation

By Scott Stephenson

Where does an idea begin? And how can a concept be nursed through a promising infancy into reality, and then kindled into a profitable innovation? Even when a questioner is armed with relevant pools of data, responses for such seemingly simple questions themselves often point to other open-ended questions.

Evolution of an Innovation

In providing data, analytics, and decision support software to leaders in energy, financial services, and insurance, Verisk Analytics develops keys to those questions—keys that can unlock the seeds of innovation within an organization. We draw on dozens of petabytes of unique data and deep domain expertise to deliver innovations that our customers can ultimately integrate into their workflows. Companies and governments use Verisk’s solutions to help make better decisions about risk, investments, and operations with greater precision, efficiency, and discipline.

Lasting innovations often take root from evolutions in thinking. Data analytics is not a program or bolt-on accessory for business operations, but a strategy that should be embraced as sinew and fiber through the body of an organization. Instead of an afterthought, analytics has to stand at the leading edge of business strategy, and it usually works best when embraced throughout an organization and not just by a data analytic “elite.” A strategic behavior on the part of any business in the 21st century should be to make use of data seamlessly from within and outside the enterprise to generate new and unique insights.

Sharing a mindset

A very good question for corporate leadership to ask is, Are line managers thinking, behaving, and sounding more like data analysts, and are data scientists and data engineers sounding more like line managers? Ideally, the business mindset and the data analytic mindset should sit side by side between the left and right ear of the same individual. But even if they’re not that integrated, a shared mindset should reinforce the notion of using data to create meaningful information and not merely to warehouse it. C-suite executives should insist that projects deliver results within nine months—data analytic methods are that fluid and powerful. A monolithic plan or “Manhattan Project” isn’t necessary to derive substantial benefits. Companies need—and can achieve—continuous, agile improvements propelled by the skillful use of data analytics.

If we accept the concept that data is like oil, then a “data refinery” is the strategic operating model for companies with digital exposure. New sources of crude data are researched and provisioned to enable an enterprise to commercialize this resource. Raw data flows into the enterprise, including customer-contributed proprietary data, purchased data, freely available data, and streaming data. After refining the raw data, the enterprise can create valuable intellectual property (IP) through proprietary processes, subject matter expertise, analytics, software, and combinations of data sets.

The “refined” data can be stored in databases appropriate to the type and scale of the data—just as oil products such as gasoline, heating fuel, and motor oil are stored in tanks. Refined data products can then be distributed to customers to provide analytic insights or used to develop new products. And of course, full understanding of any legal, regulatory, and contractual restrictions surrounding the use of the related data remains critical.

Looking ahead, the advent of remote imagery, textual analysis, connected homes, and the Internet of Things, among other developments, will make risk decisions even more targeted and more real-time. Analysts estimate that current North American smart-home penetration may grow to almost 30 percent by 2020. In time, we’ll discover how this technology can affect risk assessment. What’s the optimal mix of devices to lower overall risk or claim severity and frequency? Do connected-home systems that are professionally monitored deal with theft better than self-monitored systems? Many questions haven’t yet been answered, but the solutions are coming.

Different data—and faster

A key idea to remember is that technology is providing us with different kinds of data points, in greater volumes, and with greater speed. That information enhances analytic models and, as a result, can help improve risk selection and assessment. One obvious example involves fraud. Fraud fighting is usually a network problem, with the bad guys preying on various market participants by using multiple identities. Early detection can mean containment of fraud and a reduction in payments to fraudulent actors. The use of innovative analytics can proactively detect networks of special interest and flag organized fraud activity.

In the traditional approach, fraud investigators start with single suspect data points, such as name or address. Then they reactively build a network with the associated data. A new approach makes the process proactive by scanning data sets to detect fraudulent networks and using advanced analytics to identify their attributes, then scoring and prioritizing those networks based on the fraud potential. Finally, when we overlay data from social media or data derived from mining the text of claim adjusters’ notes—and then apply the new data-enhanced analytics—we have a much more comprehensive toolbox to use in fighting fraud.

With that said, if a company wants to be competitive today, it must strive for something we at Verisk call the n+1 data set. If a company’s data set has a certain number of elements (n), that set should be stretched to include one more. Elements must continually be added, advancing toward the next layer and adding richness to its analysis. The process requires investment in data resources, analytics, technology, and people. But the return on investment can mean thriving, rather than failing or merely surviving. That’s true for all industries but especially so in data-driven industries such as energy and financial services.

In terms of energy, relentless pressure to lower coal consumption will likely intensify competition in the remaining coal market. Some producers may meet this challenge using a conservative approach and focus on extracting coal. But as market opportunities fade, only low-cost suppliers with access to quality coal and superior market knowledge will likely succeed. And yet there’s another way—energy diversification—that employs an innovative strategy powered by the latest data and analytics. Diversification for coal producers is a “stretch” strategy aimed at joining a wider, non-coal energy market that’s growing, not declining. So again, here we clearly see the value of the n+1 mindset.

Other lines of business, functions, and industries can apply such data-enhancing technologies as well. For example, most new vehicles contain telematics devices that transmit vehicle-generated data, including speeds, locations, and risky maneuvers, to their manufacturers. Some insurers use the data to refine their pricing structures, reward safe driving, modify risky behavior, identify fraudulent applications or claims activity, or dispatch emergency assistance or adjusters to accident locations.

An innovator evolves

The mission of Verisk Analytics is to help customers understand and manage the risks they face every day. That goal has remained essentially the same throughout our company’s 45-year history. We support a broad range of markets, including agriculture, risk management, financial services, energy, government, and property/casualty insurance. We divide our services into two segments: Risk Assessment and Decision Analytics. Verisk’s Decision Analytics business serves customers in a variety of industries with tools that help them make informed decisions about managing their assets and the associated risk. We offer products that help with predicting future losses, selecting and pricing risk, detecting and preventing fraud, and quantifying losses that have already happened.

Today, in dozens of offices across the United States and around the world, Verisk comprises nearly 6,200 people. Our company’s workforce includes actuarial, data management, and statistical professionals; scientists; legal experts; computer programmers, developers, and technicians; insurance policy experts; claims and fraud experts; property and community survey field staff; sales and service representatives and consultants; and support staff for all our functions, products, and services.

Four decades of ceaseless innovation were recognized in 2015, when Forbes named Verisk Analytics to its list of the World’s Most Innovative Companies, a distinction repeated in 2016. At Verisk, the seed of innovation began with a question that prompted further questions—and created a company culture that finds solutions.

Scott Stephenson

Scott G. Stephenson is chairman, president, and chief executive officer of Verisk Analytics.