Tech Firm Aims to Help U.S. Life Insurers Do More With Their Data
SparkBeyond says it has a research engine that's great at spotting patterns.
A data analysis firm is making a major push to win clients in the U.S. life insurance sector.
The company, SparkBeyond Ltd., has put out a white paper describing, in general terms, what its artificial intelligence-based research engine system has done for insurance industry clients, including life insurance company clients.
SparkBeyond says for example, that it has helped clients locate the data sources needed to develop new types of insurance pricing, customer service and underwriting tools,
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One project, for example, involved helping a U.S. life insurer develop an accelerated underwriting process that reduced the need for applicants to provide physician’s statements, according to SparkBeyond.
SparkBeyond was based in Netanya, in Israel, and is now based in New York. The company’s team members have a number of AI-related patents, in connection with tasks such as generating AI hypotheses about a set of raw geographic data, finding the strange entities in a set of data, and using raw data to generate features for use in an automated machine learning process.
SparkBeyond is part of the Microsoft Partners Network program, and it says it developed the white paper together with Leon Fretz, the insurance sector director at Microsoft.
Guy Zinman, SparkBeyond’s insurance lead, said in an email interview, said in an email interview that one thing his company has learned is that client companies that want to make good use of AI data analysis systems need to executives who are enthusiastic about establishing a data-driven culture.
“Achieving business impact is a multidisciplinary team sport,” Zinman said. “Analytics can’t be conducted separately from the business context, and cross-functional teams of actuaries, data analysts, data scientists, and business unit owners are essential in embedding data science into business strategy.”
Related
A copy of the SparkBeyond whitepaper is available here.
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