aiTech Trend Interview with Glenn Hofmann, Chief Analytics Officer at New York Life - AITechTrend
glenn hofmann newyorklife

aiTech Trend Interview with Glenn Hofmann, Chief Analytics Officer at New York Life

Can you tell us more about New York Life and your role as Chief Analytics Officer?

New York Life is America’s largest mutual life insurance company and one of the largest life insurance companies in the world. New York Life and its subsidiaries provide insurance, investment, and retirement solutions that help people at all stages of life achieve financial security, delivered through financial specialists nationwide. A Fortune 100 company founded in 1845, New York Life has a workforce of more than 23,000 financial professionals and employees. Being a mutual company means New York Life operates for the benefit of current and future policy owners— not outside investors, because the company doesn’t have any. The mutual business model allows New York Life to make long-term decisions without the distraction of shareholders seeking short-term returns, which is ideally suited for the type of long-term promises that New York Life makes to policy owners. As Chief Analytics Officer and head of New York Life’s Center for Data Science and Artificial Intelligence (CDSAi), my role is to provide oversight and guidance on data science and AI-driven projects across the enterprise with a primary focus on supporting our foundational life insurance and strategic insurance businesses. This involves regularly collaborating with our stakeholders to identify existing processes to use AI algorithms to provide savings or efficiencies, staying abreast of new technologies, and providing the data science team with the necessary tools and support to serve our internal customers. Data science education and supporting an active data science community are big parts of what our team does whether that’s one-on-one with our business partners talking through a model-driven approach or facilitating data science educational tracks, workshops, and events that we regularly host for all of New York Life.

Can you tell us about your journey into this market?

After receiving my PhD, I started my career as a professor and quickly became interested in applications of statistics within the industry. My first real introduction to consulting was within the wine industry while I was teaching at the University of Concepcion in Chile. Over the next few years, I moved between consulting and leading increasingly larger teams within financial institutions. Mid-career, I received an MBA and stayed in the financial industry. With that said, I’ve learned that being an effective leader requires putting on the consulting hat on occasion and my past experience has served me well in my current role. An effective CAO needs to be able to assess a situation, provide guidance, and evaluate the viability of potential solutions. Every business area is on their own journey in using AI whether through need or other priorities. Our team works hard to meet each area where they are and guide them through the process. I’m fortunate that my career has taken me to New York Life to build and oversee the CDSAi team. I worked in the insurance industry before this, but life and property & casualty insurance are distinctly different. The team has been successful in developing novel data science solutions to address New York Life’s business needs. Building this team from the ground up and establishing the technology infrastructure to deploy our models has been incredibly rewarding.

How does New York Life use AI to tackle data problems?

We work incredibly closely with our business partners to understand how an AI solution can provide incremental value. We prioritize our work based on the potential value while balancing time for research and proofs of concept. For example, as a life insurance company, we evaluate and price for the risk we take on when issuing a life policy. The CDSAi team works closely with the Underwriting department to identify opportunities for AI algorithms to be used to minimize that risk. In response to the virtual environment brought on by the pandemic, our efforts toward model-supported underwriting were accelerated to enable the company to underwrite even more life insurance policies without the use of fluids while still mitigating risks. Data science is also used to support the efforts of our industry-leading agent field force. As an example, leveraging geospatial data science using our deep internal data combined with external data allows us to identify growing populations where we might want to expand our presence.

Top three emerging trends that I see are:

1. Cloud based data and solutions

2. Scalability of models and integration with APIs for real time decision making

3. Data privacy and AI ethics These trends are all very much interrelated.

How is Data Infrastructure effective for AI?

We work hand in hand with New York Life’s Technology team. They have been great partners for us when it comes to both data and technology. We are actively involved in the strategy for a common language and understanding around our data and for ease of accessibility for teams that leverage such vast and varied quantities of data for model development and usage. We have also partnered in building the infrastructure for data science models to be developed and deployed at scale. For example, last year we deployed a cloud-based computing environment for all aspects of the data science lifecycle. This allows us to quickly deploy models and get the output as an API to enable real-time decision making. Technology and CDSAi work together for business stakeholders to have an easy, seamless experience with the data science process.

What breakthroughs in the AI/ML space are you most looking forward to from a technology perspective?

I am eager to see how natural language processing (NLP) continues to be used as a tool to aid in reading and interpreting text. Communication is, of course, critical to business and we can best serve our policy owners, financial professionals, and employees with personalized responses in a timely manner. Not only can NLP be used for real-time communication or navigation, it can also be used for research and to read supplementary documents that provide rich sets of data. This is not an area of focus at New York Life, but on a personal level, I am intrigued by the ability of AI to help people with disabilities. A recent New York Times article highlighted how machine learning helped a ​man who has not been able to speak for nearly 20 years communicate by reading signals from electrodes implanted in his brain. This illustrates the vast potential of the AI/ML space to continue to help humanity. From advancements in voice-assisted technology to help with everyday tasks in the home to enable better communication for those who have speech impediments, AI is helping to provide independence and bring people closer together.

How do you keep pace with the rapidly growing AI Solutions products for businesses?

The team and I are always excited to learn about new AI solutions becoming available. We keep an open mind to new products and solutions and enthusiastically test to potentially bring them in-house. The CDSAi team is encouraged to attend conferences, keep up with developments, and have a finger on the pulse of the data science industry at large. We are also actively engaged with and enjoy meeting, encouraging, and mentoring start-ups in the data science space.

Do you have some final thoughts?

The team and I look forward to seeing how the use cases of data science and AI at New York Life, the insurance industry, and beyond continue to grow and to playing a meaningful role in the ongoing evolution of the discipline. The insurance industry has a wide variety of interesting business opportunities that can be supported by AI-based solutions. At New York Life, our Center for Data Science and Artificial Intelligence has rapidly expanded over the last five years in terms of size, scope, and technology. We are now at a stage where we are operating at scale with an efficient process that moves us from an initial idea to an eventual deployment of a model within a business and we’re very much entrenched in business’ decision-making processes. The team looks forward to delivering further value in areas that previously did not employ machine learning. It’s an exciting time at New York Life as we continue on our AI-enabled journey. The opportunities for the use of data science and AI continue to grow exponentially and we’re ready and willing to serve as evangelists. When it comes to our space, the future is certainly bright.