OSFI official warns of risks from big data

The use of “vast amounts” of data, from claims and customer behaviour, to make underwriting and product decisions, carries some risk for property and casualty insurance carriers, especially when underwriters use models with a  “small number of theoretical foundations,” suggests an official with the Office of the Superintendent of Financial Institutions (OSFI).

P&C carriers use “vast amounts of claim experience and, increasingly, vast amounts of customer behaviour data to support the pricing of its products,” according to a copy of a speech that OSFI deputy superintendent Andrew Kriegler  was scheduled to give Thursday at the 2014 Property and Casualty Insurance Industry Forum in Cambridge, Ont.

“The use of this data together with increasingly sophisticated models is driving industry’s evolution towards increased efficiency and competitiveness,” Kriegler said, but added “one consequence of increased efficiency is increased volatility.”

Kriegler suggested there is concern from regulator about banks using models to measure capital adequacy.

“Our ability to collect data, to process it and to look for patterns within it, is getting better and faster by the day,” he said of the use of models by banks. “And if the data is there, it is going to be used.”

Banks, he added, face challenges when using models, including “short data histories, low frequency, high severity events and unstable statistical relationships.” P&C insurers, he suggested, face similar risks.

“While catastrophe (CAT) risks are the obvious example, others can also appear when unexpectedly correlated liabilities arise due to changing societal norms or judicial interpretations,” Kriegler said. “So there may be things that the two industries can learn from each other.”

He recommended p&c professionals ask themselves whether their models they use are “well designed and properly maintained,” and whether other carriers are getting the same answers at the same time.

“The increasing use of big data to support product design, underwriting and pricing decisions — especially if they are based on models that derive from a small number of theoretical foundations — has the prospect of taking the longstanding competitive underwriting cycles and making them both more frequent and more severe,” he said.

The risk, he added “is exacerbated when the dataset supporting these models is lean and the reality that underlies the data is subject to potential change, as in the case of new or expanding products.”

The riskiest insurance products derived from such data, he suggested, are those that cover infrequent, high-loss events, such as catastrophes.

“Earthquake cover – where risk assessment is supported by a handful of modeling approaches – is one example,” Krieger noted. “Overland flood is perhaps even less supported by comprehensive and stable data sets.”