AIG: Data Science in the Insurance Industry and Financial Services (CXOTalk

Murli Buluswar, former Chief Science Officer at AIG, and currently Senior Executive Advisor the Boston Consulting Group (BCG), speaks with CXOTalk co-hosts Michael Li, CEO of The Data Incubator, and Michael Krigsman about data science and innovation in the insurance industry. How can insurance and financial services companies adapt and thrive in a world of data and digital disruption?
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Buluswar is a trailblazing and innovative leader. Since 2014, he’s helped AIG evolve from a ‘knowing culture’ to a ‘learning culture,’ from an organization reliant on human judgment to a firm that benefits from its institutional risk insights manifested through data models. Currently, he is a Senior Executive Advisor at the Boston Consulting Group
From the transcript:
Murli Buluswar: The way I would reframe that is you help them fundamentally recognize that this is not just a separate pillar that you should be thinking of as being incremental to how you will shape your business strategy. These competencies are in the very near future or, in fact, even in the here and now. In effect, a mitochondria that will shape the energy and the life that your firm will have in terms of its sustainability in a world of data and tech driven disruption.
The challenge then is that typically in many of these large institutions, you've got leaders who have risen to those senior positions on the basis of historic experiences, which are less relevant if you extrapolate them to the future. And so it really does become an issue around having the humility to develop much more of a learning mindset; and recognizing that the more ambitious you are in terms of really re-sculpting and reshaping your competitive positioning, the more you have to be willing to break glass based on the insights that you achieved through data science.
Michael Li: You need a broad swath of the organization to understand the value of data, how you use data--think about some of the issues that Murli and I were just talking about earlier--that really embrace taking time to have their employees learn about data science and big data. On the cultural side, actually, I'd be curious, Murli, to ask you this question. I think one of the things that's maybe unique about insurance or banking is that there is kind of a legacy of data around the actuarials, around the statisticians. How does that change the dynamic of creating a data culture when you have a legacy group that's somewhat already steeped in this?
Murli Buluswar: I think there are two parts to that, Michael. One is, how does that change decision-making today, and how should that change decision-making tomorrow? If one were to zoom out, in general I think the actuarial function, the profession, and the exams have not embraced, from my point of view, the power of data science in its totality the way perhaps they should. Maybe they will, looking into the coming few years.
The other piece of it is, if you disaggregate the entire value chain of insurance, there's data science that can be applied to many, many, many aspects of it that can fundamentally shape the sophistication, timeliness, [and] granularity of decision-making in ways that the industry could not have imagined a decade ago. To me, the role of data science is very, very widespread, even if one were to dodge the traditional domain of the actuarial sciences. Where I'm hoping the industry is going to head toward is, rather than have this mindset of creating rigid silos or pillars, see that the competencies are interchangeable and they're one in the same. Let's actually move to a world where we're challenging; we understand our assumptions and are challenging those assumptions to shape the caliber, effectiveness, and efficiency of decision-making as opposed to hanging our hats on what titles we've got, what professional credentials we've got, or what academic experiences we have because those are an interesting starting point, but are really not particularly relevant in a world where everything around us is changing at a more profound pace than ever before.
Michael Li: With the actuarials, I think that a lot of the really farsighted ones, the ones who are really looking to the future, seem to really understand this and are embracing a lot of these new techniques around data science, around big data, really looking to challenge the assumptions that maybe their own discipline has ingrained into them through indoctrination. [They're] really leveraging the existing knowledge that they have, this really strong knowledge of probability and statistics, and then seeing how they can apply that to the data science, which of course is very rich in probability and stats.

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Guy is a GIG