The future of commercial fleet insurance lies in the convergence of telematics with Big Data analytics. But it will require insurance companies to make investments in systems and expertise
According to MSA Research Inc., the total 2012 GWP for auto premiums in Canada was $20,651,227, with about 13% of the mix of business attributed to non-private passenger auto. In March 2013, an actuarial analysis performed by J.S Cheung and Partners, as well as KPMG for the Insurance Bureau of Canada (IBC), revealed interesting results around the performance of both non-private and private auto sectors in Ontario. The overall combined loss ratio for this line of business stood at 101.6%. Broken out, there was a distinct difference with personal auto at 102% and non-private passenger at 97%. The net income, as well as the average return on equity (ROE), for non-private-passenger was double the value of the personal auto return of 4.9%. Of course, every point of profitability improvement on this line of business has significant impact on an insurer’s results.
Beyond just the profitability level, having access to a large quantity of diverse information can be thrilling for those who see the potential use of analytics as the future of our industry. As insurance company executives sit down with their teams to carve out crisp business plans, including the IT spends necessary to give us the required business return in investment (ROI), The will consider some of the following questions: What will the data tell us? What do we really want to know? How do we manage all that information?
The key when working with large amounts of information is to really understand what threatens the potential positive returns to a company’s current and future business plans as team, business unit and company.
In personal lines, the data points that can be used range from the traditional—census information, household income, real estate values, vehicle sales, crash statistics and geo mapping—to the innovative: location-based marketing, social-media usage and 3D Google mapping. The newest addition to the mix is the use of telematics devices in private-passenger vehicles, giving a pay-as-you-drive (PAYD) option to consumers. In the United States, Progressive’s 2004 investment in TripSense has evolved into Snapshot, which has now reached 1.2 million policies and $1.8 billion in premium. Here in Canada, the airwaves are being populated by Desjardins’ Ajusto as the newest player, working to penetrate the information-overloaded generation for market share.
Given the profitability pressure on the private passenger auto line, cracking the data code for personal lines is a massive opportunity for insurance companies. Fortunately, the information overlays that provide that road map to better ROIs can be teased out at a reliable and scalable rate for personal lines insurers. Companies are working with a transaction-based and highly-regulated product that provides statistically-reported class and loss information as a sound foundation to build upon. On the Big-Data analytics side, the use of discount qualifications such as honour student, claims free and multi-policy discounts could provide additional insight into the traditional information found on the auto application, for example. Motor vehicle reports and claims history also give the historical and loss trending real perspective. Online inquiry sites provide rich marketing data around reaction triggers to targeted content, helping to bring the target customer into clear focus. By pulling a “proven set” of risk indicators from a USB download, insurers can provide true PAYD to a specific group such as the 50+ experienced drivers and the non-street-racing under 25s. These are example groups where the additional overlay of a telematics device on these drivers’ cars can really prove its worth.
It’s not how many data variables are pushed through pricing algorithms and predictive analytic databases. Rather it’s the scale or volume of those “soft data points” with driver-specific information that gives companies a clear picture of their exposure. Below is an example of how this works and demonstrate how data points can be used together:
Ex: Driver A.= Age+Postal Code+Vehicle (make, age, safety features)+Distance Driven+Usage (time behind the wheel)+ Known Risk Indicators (Hard Breaking, Speeding)+Route = Premium
The use of Big Data can be highly complex and difficult to manage when variables are diverse, vast in scope and there is a massive amount of volume involved. This can be equally true about the use of Big Data and the future of commercial auto fleet insurance in Canada.
Large commercial fleets, especially heavy commercial fleet operations, produce enormous amounts of data. As competitive pressures on costs began to erode fleet operators’ profit margins, an entire industry segment was introduced to the power of data analytics as a method to improve operational expenses. This awareness took place decades before the use of data analytics for private passenger vehicle use was tested in Europe and the United States. Unfortunately, even though the benefits were apparent, many companies found the initial cost of on-board devices and back office technology prohibitive at that time (see more on the history of telematics affordability on page 46.—Ed.) and the focus became streamlining existing manual processes to help manage information and costs.
Customs information, hours-of-service requirements, federal and provincial carrier profiles, IFTA reports, weather patterns, high-theft-alert zones, traffic reports, weigh-scale requirements, engine performance, tire pressure, and fuel consumption are just some of the types of data available for accumulation for commercial fleets today. Unlike Personal Lines auto, which collects information on a single active driver’s behaviors to be monitored, commercial fleets apply user-based interfaces (UBI) in tandem with other technologies to create an active fleet management system (FMS). These systems can monitor active driving characteristics such as navigational skills, as well as the reactive or defensive driving skills around accident avoidance. In addition to the driver, the vehicle itself has hundreds of monitoring points that can create an alert to the driver or fleet operator that maintenance work needs to be completed, and measure the functional efficiency and safety of the vehicle while on the road.
Beyond tangible asset management, new emerging issues are presenting unique challenges to the heavy commercial fleet operations. Insurance companies will need to be able to capture data for their own use from outside sources or information providers and merge that into an effective game plan for their core business. Some examples include:
- traffic delay and altered route management
- efficient toll charge and inspection stops
- optimized driving green approach
- corporate identity protection
- accident scene reconstruction
- cross-fleet communication and coordination
- vehicle theft or cargo crime alerts
- driver health and wellness
- changing legislation on environmental remediation and reporting.
In the not so distant future, the number of commercial fleets employing data management tools and services will dramatically increase as the price point for these programs, which include fleet management systems, continues to drop. Data-management tools are also becoming more accessible—a smartphone is now a low-cost solution for the light commercial vehicle space. Portable on-board diagnostic systems (OBDII), handheld PDAs and portable navigation devices are viable options for data output and input for medium and heavy commercial fleets. Large hardware expenditures are quickly being replaced by cloud computing, available through outsourced service providers. The next generation of drivers will have grown up using similar personal-use technology in app format and will have a comfort level with its capabilities. As for government regulation, interface requirements are being discussed and put in place by both the US and Canadian governments to facilitate faster border crossings, asset tracking and homeland security enhancements.
All of this information is out there and, with the customer’s assistance, could be utilized by insurance companies for a number of initiatives beyond pricing. So what does this all mean? Will insurers and brokers capture the right data from customers? Will they understand the information and produce a product that makes sense to the consumer? That remains to be seen. The insurance industry is notorious for having to work with legacy platforms and patched together processing systems. It will take a robust set of pipes to fully integrate an analytics strategy that will allow for real transfer of information with brokers and customers. Being competitive on pricing and service alone will not be enough when the commercial customer base is at a higher level of technological innovation than our industry.
There is enough volume and specialization in the commercial auto portfolio to make the opportunity around effective data management and utilization well worth the investment in high performance analytics and innovative talent. Improving the profitability on a book of business can pay off significantly in the long-term, as demonstrated by the current difference in the performance between commercial and personal auto in Ontario.
Angelique Magi is the national vice president of strategic initiatives at The Guarantee Company of North America, with a focus on the transportation sector. Prior to joining The Guarantee, she was national director for transportation at a major global insurance company. She can be reached at Amagi@gcna.com.
Copyright 2013 Rogers Publishing Ltd. This article first appeared in the October 2013 edition of Canadian Insurance Top Broker magazine