Insurers’ risk management practices are becoming increasingly reliant on data and analytics, according to a new report from State Street that surveyed more than 400 insurance executives about their capabilities in this area. Only 13% of insurers surveyed considered their firms’ enterprise-wide data management to be a significant strength and only 19% are confident in their multi-asset risk tools, the lowest proportion of respondents from a pool of asset managers, asset owners and alternative managers. Against that backdrop, insurance executives recognize the need for better, stronger data systems, with 82% of firms citing data and analytics as a key strategic priority for their business.
According to the report, investment in data and analytics amongst insurers is expected to grow. An overwhelming majority of respondents(81%) stated that they intend to increase spending on data initiatives in the coming years. State Street saw a threefold increase in the number of insurance clients in 2013, demonstrating the growth in popularity of data-driven risk solutions amongst insurers.
“Fundamentally transformed markets, a push into new asset classes and a more stringent regulatory environment are all accelerating change and contributing to a rise in demand for data and analytics capabilities amongst insurers,” said Jeff Conway, EVP and head of State Street Global Exchange. “Risk management has always been at the nexus of underwriting and investment, and insurers know what is at stake. The challenge ahead is how to turn fragmented IT systems into a strong and flexible platform capable of adapting to the demands of a more complex investment climate.”
“To build an effective data driven business for 2014 and beyond, insurers should focus on building a stronger foundation,” said Scott FitzGerald, EVP and head of State Street Sector Solutions, Americas. “They can do this by improving risk tools with multi-asset class capabilities, developing solutions to manage regulation globally, accelerating investment decisions, and developing a scalable data architecture.”
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