At Chubb Insurance, there is a strong impetus to implement predictive modeling and analytics throughout the organization, which is driving the constant need to recruit talent, explains Upendra Belhe, Ph.D., chief enterprise business analytics scientist. But hiring talent for predictive modeling and analytics is different from hiring other types of talent, he says.
While the need for talent is great, the competition is intense and the talent is scarce, having the mathematical skills and degrees isn’t quite enough to land a job on Belhe’s team. At Chubb, the need to acquire talent is tempered by Belhe’s insistence on a strict set of secondary traits, including common sense, empathy, maturity and patience.
”People are quickly beginning to understand that taking the data and predicting into the future is only a small part of the game,” Belhe says. “The bigger part of the game is story-telling, and I think more and more stories have been written and are being written with this data.”
In addition to the ability to conduct statistical analysis or build predictive models, Belhe says candidates also need to demonstrate the right level of empathy for the business users who will use the models. Because predictive modeling and analytics are such disruptive technologies, Belhe says, they will put many business assumptions at risk, Belhe explains. That, combined with the insurance industry’s culture, which tends to be more conservative and hierarchical, makes maturity, which includes humility and the ability to listen, an essential ingredient for determining the candidate’s compatibility with the team and the company, he says.
“Have they been in a situation where their audience has been completely business oriented and not at all analytically savvy? Can they make a story line to explain it to our business users? That is where our need stands. And that makes it slightly harder, because that level of maturity is very difficult to judge only from resumes.”
Belhe says the involvement of the business people, the users of the models, is hugely important to him and his team and a determining factor in the success of the models, and the company, which increasingly relies on them for profitable growth.
Many modelers come with deep statistical analysis backgrounds or statistical validation points of view, Belhe says. “I think that is important, but that is not where we begin. We begin with very common sense-ical data analysis and working with the business users and understanding where the pain or the opportunity is. What that means is looking into the descriptive parts of the data, and what that data provides us from a story point of view. That is very important to us,” Belhe says.
“We want to be very delicate with that process because our business users may have years and years of experience into this. They’re pretty smart into their functions. And if you say you want to address some of the functions you find questionable, rather than you bringing disruption to the table, provide [business users] the power of disruption. Offer, and see where they would want to use that disruption to improve.”
Belhe says he frequently offers hypotheses, based on ideas posed by business uses, which helps diffuse some of the inherent tension in these processes.
“It’s okay if it is disproved, but let’s see what does the data say. When we started unfolding that, some good insights start flowing in,” Belhe says. “We grow with their understanding, and we mature into their understanding as we go along,” Belhe says. “So, we believe that instead of doing analytics for them, we like to do it with them. And that is our model. We want to use disruption, but we want to use disruption the way business would want to process it,” Belhe says.
In addition to maturity, Belhe stresses the importance of patience for modelers. “Somebody who has come from purely analytical thinking, and who thinks that they get it,’ that’s a sign of immaturity to us. We look into the other traits people have: patience; and can they listen, and ask the right questions, and really understand what the process should be. We believe that whatever the outcomes are of analysis, we cannot make them actionable unless we have such a careful approach towards the business.”
As the field of predictive modeling matures, so does the technology and the company’s need for talent, which always is changing, Belhe says, describing the progression from actuarial sciences, generalized linear models, pricing analytics, and now the emergence of big data and unstructured data.
“Smart people understood the difference between hype and reality, and that changed the talent equation very quickly,” Belhe says. “With this infrastructure and external data, how do you create those stories? That is where I think you will find the future winners in the insurance domain. They are the people who understand the difference.”
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