Applying AI & Data Science at New York Life Insurance

The speaker discusses the use of machine learning in various areas of their organization, including sales, finance, underwriting, recruiting, and fraud detection. They emphasize the importance of building relationships with the business, having infrastructure for data scientists, and experimenting with different business models. The speaker also shares anecdotes about specific challenges they faced in implementing machine learning.

• Machine learning is used in various areas of the organization, including sales, finance, underwriting, recruiting, and fraud detection
• Building relationships with the business is important for successful implementation of machine learning
• Infrastructure for data scientists is necessary, including data curation and development and deployment environments
• Experimentation with different business models is important
• Challenges include dealing with unstructured data and separating relevant information from irrelevant information

The speaker shares a specific challenge of dealing with unstructured medical text data, such as attending physician statements, which can be hundreds of pages long. They discuss the difficulty of separating relevant medical information from irrelevant information, such as a patient's cold versus a heart transplant. This challenge required expertise in natural language processing to solve.

The New York Life Insurance Company is the third-largest life insurance company in the United States, manages over $700 billion in assets, and invests in one of the most forward-looking data science teams in the insurance industry. Glenn Hofmann is Chief Analytics Officer at New York Life and has spent over six years positioning New York Life's data science team to impact the bottom line, productivity, and even recruiting. Hofmann joins Melisa Tokmak, General Manager of Scale Document AI, to talk about New York Life’s data science journey. Learn how Hofmann has deployed advanced machine learning solutions that allow the company to easily access and analyze the data at scale. New York Life’s data science team evaluates the risk taken on when issuing a life policy. The group works closely with the underwriting department to identify opportunities for AI algorithms to be used to help manage that risk. Among other achievements, the data science team helped build the infrastructure for data science models to be developed and deployed at a massive scale. Also, the team has created a data hub, a consolidated library of data assets, refined to an analyzable format.