To control costs and optimize insurance availability an overwhelming number of risk managers feel their organization must conduct deeper research into their risk to reap the full benefits of analytics, according to an online survey taken by insurance broker Marsh.
Nearly 80 percent of risk managers attending a Marsh webinar, "Using Data and Analytics for Optimal Risk Management," says their companies need to take a closer examination of risk-related data.
Of companies employing a risk manager, close to 44 percent say they do not have a set dollar-amount threshold for unexpected losses and 29 percent do not know if their company is aware of how much risk they can take on—about the same number that do quantify and share risk information with their insurance managers.
“When it comes to property risk, the market is driven by net catastrophe exposure for wind, earthquake and flood,” says Ryan Barber, Marsh’s Property Practice managing director. “Not only is the quality of that data important, but also its completeness. In its absence, [catastrophe] models will default to cautious assumptions that can distort underwriting perception, inflate premiums and reduce available capacity.”
Risk analysis, which predicts how natural and human-caused catastrophes will endanger property and business production, can help align the risk department’s loss-mitigation expertise with corporate growth objectives.
“We use our analytics for internal discussion with leadership for the foundation of our [risk] program strategy,” says Michael Mulray, manager of insurance programs for General Electric, which recently revamped its Workers’ Compensation program. “We went to our actuaries and asked them to tell us the perfect amount of information needed. We focused on the concentration of employee risk at our individual locations.”
Mulray says GE didn’t take time to review data quality about three years ago, when the company last revised its Workers’ Comp strategy. During its last review, however, Mulray’s department took a “deep dive” into analytics to incorporate information about site security, shift work, and the number of employees at any location at all times.
“The outcome was quite amazing, because in some locations, overall estimated loss dropped by over 50 percent,” he says. “If underwriters had the wrong data and used it to make decisions on pricing and structure, they would be misleading management about what their true risk is.”
Not only did targeting location-specific data add credibility to the risk department’s role in corporate decision-making, he says, it led to savings in premiums as well.
Digging into data is crucial for large corporations, such as GE, which have complex layers of coverage and can suffer if a single unforeseen, or “black swan,” event derails the manufacturing and transportation process, but it can also benefit smaller companies.
“Smaller companies are just as engaged [in analytics] as larger ones,” says Claude Yoder, head of Global Analytics at Marsh. He says the dollar amounts may be smaller, but they will still derive the same amount of benefit from the analysis.
“Because GE is large, the issues we face with data and analytics are different from those faced by small companies,” says Mulray, “but it is important for them to understand that we all use the same principals, regardless of size.”