Why AIG learned to embrace the art of data science

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We always hear about the power of corporate culture, and how it can overwhelm efforts to introduce change into the enterprise. Insurance companies often have cultures all their own, seemingly paradoxical in that they have long been anchored to risk management thinking, but at the same time highly people-centric.

Insurers have always struck a good balance, but how will it stand up in the age of the digital enterprise?

That’s the balance Murli Buluswar, former chief science officer at AIG, sought to maintain as he built one of the industry’s first high-functioning data science operations. In a recent chat with CXOTalk’s Michael Krigsman, he discussed how he 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.

Data science can help insurers in a number of ways, Buluswar says. The challenge “is trying to predict your cost of goods sold at the point of sale.” The insurance industry actually has a longstanding tradition in analytics that is tied to the actuarial function, he says. The key is to expand this analytical thinking across other domains, enabling “more structured, granular, sophisticated, consistent decisions -- in sales and marketing, as well as in pricing, underwriting, and in claims.”

Insurance, as with other industries, has a “sales or distribution channel. You have a product channel that is around pricing the product. Some of that is around your cost of goods sold, and some of that is trying to understand the market's appetite and the customers' demands, or demand elasticity.”

With today’s tools and platforms, data science principles can be applied to various aspects of the insurance value chain. “The role of data science is very, very widespread, even if one were to dodge the traditional domain of the actuarial sciences,” Buluswar says. “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.”

With data science, insurers can leverage real-time insights to forge partnerships to share this information, he says. “The most obvious example of that is the role that sensors can play in providing real-time feedback to drivers of vehicles in a way that hopefully reduces risky driving and mitigates the likelihood of accidents. To me that is the true power of data science in insurance. The beauty of that is not only does it mitigate accidents from happening, or adverse events from happening, but what it reduces the cost of insurance and expands the reach of insurance to a much broader population. To me, that's a beautiful thing if you think about society having a much higher level of financial protection across every aspect of our lives.”

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Data science Predictive analytics Analytics Claims Underwriting Real-time data Insurance technology AIG