In 2015, New York Life Insurance Co. began building up a data science team to investigate the use of predictive models to improve efficiency and increase productivity.
There were quite a few deployments of predictive models across the company with a little artificial intelligence to aid in automation. Most of the projects were not centered around machine learning and AI but traditional data science. Models were generally used to support actuarial assumptions, to aid agent recruiting, and enhance the purchase experience (e.g., bypassing the need for blood tests for underwriting).
General AI was also used in creating marketing campaigns to determine the most appropriate audiences to target.
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In 2015, New York Life Insurance Co. began building up a data science team to investigate the use of predictive models to improve efficiency and increase productivity.There were quite a few deployments of predictive models across the company with a little artificial intelligence to aid in automation. Most of the projects were not centered around machine learning and AI but traditional data science. Models were generally used to support actuarial assumptions, to aid agent recruiting, and enhance the purchase experience (e.g., bypassing the need for blood tests for underwriting).General AI was also used in creating marketing campaigns to determine the most appropriate audiences to target.To read this article in full, please click here Read More Computerworld
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