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Risk management strategies could benefit from big data


NEW YORK — While insurers have used data analytics for years in order to more accurately underwrite property/casualty risks, their commercial policyholders are only just beginning to embrace “big data” as a risk management tool.

Less than one-third of businesses currently use predictive modeling and other data-driven analyses to inform their risk management strategies, according to a 2014 study published by General Electric Co. and Accenture.

However, panelists at Business Insurance’s 2015 Risk Management Summit in New York noted on Thursday, a thoughtful and sophisticated incorporation of data analytics can substantially enhance traditional risk management functions, including insurance program design and purchasing.

“It starts with determining what it is you’re trying to accomplish,” said Monte Dalton, vice president of sales at Lithia, Florida-based Ventiv Technology Inc., a risk management software provider formerly known as Aon eSolutions.

For example, by aggregating and carefully analyzing all departmental loss histories, claims and other risk-related data sets, as well as external data collected from specialist service providers, risk managers can not only more closely tailor their company’s insurance coverages to its specific needs, but justify their purchasing decisions to senior executives.

“We think a lot about insurance programs and how companies structure them, and how they make decisions on what coverages to keep and what they want to cut, what kinds of retentions do they want to have, and so on,” said David Heppen, managing director of global analytics at Marsh L.L.C. in New York.

“I think the best use of big data is boiling all of this information down in a way that allows the risk manager to say that the reason they made a certain decision was to protect the company’s balance sheet in a certain way. When you put it in that kind of financial speak, I think it’s going to have the most relevance to the company on the whole,” he said.

Additionally, panelists said a deeper integration of data analytics into a company’s risk management functions will likely enable it to present insurers with a more accurate view of the company’s true risk profile.

“When you take that out to market, instead of simply accepting an industry standard benchmark for what you should be paying, you can use that information to say to insurers that you’re not going to pay the standard, and that you want to pay far less,” Mr. Dalton said.

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