Enhancing customer care through deep machine learning at Travelers

New York-based insurance provider Travelers, with 30,000 employees and 2021 revenues of about $35 billion, is in the business of risk. Managing all of its facets, of course, requires many different approaches and tools to achieve beneficial outcomes, and Mano Mannoochahr, the company’s SVP and chief data & analytics officer, has a crow’s nest perspective of immediate and long-term tasks to equally strengthen the company culture and customer needs.

“What’s unique about the [chief data officer] role is it sits at the cross-section of data, technology, and analytics,” he says. “And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. We needed to think about those disciplines together and make progress to maximize the benefit to our customers and our business overall.”

Another focus is on finding and nurturing talent. It’s a pressing issue not unique to Travelers, but Mannoochahr sees that in order to deliver on those disciplines advancing analytics to foster a healthier business, he and his team recognize the need to cast a wider net.

“We have a tremendous amount of capability already created helping our employees make the best decisions on our front lines,” he says. “But we have to bring in the right talent. This is kind of a team sport for us, so it’s not just data scientists but software engineers, data engineers, and even behavioral scientists to understand how we empathize and best leverage the experience that our frontline employees have, as well as position these capabilities in the best way so we can gain their trust and they can start to trust the data and the tool to make informed decisions. [The pandemic] slowed us down a little, as far as availability of talent, but I think we’ve doubled down on creating more opportunities for our existing talent, in helping them elevate their skills.”

Mannoochahr  recently spoke to Maryfran Johnson, CEO of Maryfran Johnson Media and host of the IDG Tech(talk) podcast, about how the CDO coordinates data, technology, and analytics to not only capitalize on advancements in machine learning and AI in real time, but better manage talent and help foster a forward-thinking and ambitious culture.

Here are some edited excerpts of that conversation. Watch the full video below for more insights.

On the role of the Chief Data Officer:

Due to the nature of our business, Travelers has always used data analytics to assess and price risk. What’s unique about the role is it sits at the cross-section of data, technology, and analytics. And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. We needed to think about those disciplines together and make progress to maximize the benefit to our customers and our business overall. It’s a unique role and it’s been a great journey. Collectively, the scope spans about 1,600 data analytics professionals in the company and we work closely with our technology partners—more than 3,000 of them—that cover areas of software engineering, infrastructure, cybersecurity, and architecture, for instance.

On business transformation:

We perform around our current business and want to meet to be able to deliver results. But at the same time, we’re thinking about the transformation of the business because opportunities are endless as you start to marry data, technology, and analytics. So the transformation of the next wave that we’re driving is really coming from the nexus of the infinite amount of data being generated, advancements in cloud computing and technology, and, of course, our ability to continue to expand our analytics expertise. We’ve always used these things in some form or fashion to appropriately price grids, set aside a group of reserves for being able to pay out claims, and, of course, serve our customers, agents, and brokers. But what’s changed is a greater world of possibilities. On a yearly basis, we respond to about two million folks from our brokers and agents and process over a million claims per year. So if you put it all together, every one of those transactions or interactions can be reinvented through a lens of technology, AI or machine learning. So we need to inform our front lines and workers how to make the most of the information available to do their job better. It’s an opportunity to reimagine some of the work on the front line that we’re getting excited about.

On having a data-first culture:

This is not about just the practitioners of this discipline or these capabilities. This is about being able to lift the rest of the more than 29,000 people in the organization and make them better and more informed employees through being able to deliver some set of training to elevate their capabilities. So we’ve been on a mission to raise the water mark for the entire organization. One of the things we’ve done is produce data culture knowledge map training, which is designed to help our broader operation understand that the data we create daily could be with us for decades to come, have a life outside an employee’s own desk, or inform about the many different ways data has been used. We have about 13,000 employees through this set of training and it’s received great feedback from the broader organization. Plus, we’ve also started to focus on our business operation leaders and help them understand how they can better utilize analytics and data, overcome biases from a management perspective, and continue validating them so they make the best decisions to run the business.

On sourcing talent:

We have a tremendous amount of capability already created with over 1,000 models being deployed in different parts of the business, helping our employees make the best decisions on our front lines. But opportunities lie ahead, so we have to ensure we bring in the right talent. And I would say this is kind of a team sport for us, so it’s not just data scientists but software engineers, data engineers, and even behavioral scientists to understand how we empathize and best leverage the experience that our frontline employees have, as well as be able to position these tools and capabilities in the best way so we can gain their trust and they can start to trust the data and the tool to make informed decisions. One of my goals, and one of our broader team, is we want to spread the message and help the talent out there understand a lot of the great, challenging problems we’re solving for the business, and how rewarding that work has been for us. But the challenge has only increased from a digitization perspective as COVID-19 hit, which created a lot of demand. It slowed us down a little, as far as availability of talent, but I think we’ve doubled down on creating more opportunities for our existing talent, in helping them elevate their skills.