On March 30, panellists from DataRobot will come together to discuss how insurance teams can blend data science and business opportunities together to achieve success. Attendees will gain insight on why the traditional structure of insurance companies leads to missed business opportunities, how evolutions in the tech stack will change the makeup of insurance teams, how to empower the people who have the best view of business opportunities with the ability to envision the solutions, and how a new approach to insurance company data science teams can benefit business analysts, data scientists, and the broader organization.
According to Bill Surrette, customer facing actuarial data scientist at DataRobot and one of the speakers for the upcoming webinar, insurance companies tend to have two groups that are separated from one another: the people who are focused on data science and don’t know as much about the insurance operations of the organization, and the people who are focused on those operations, but don’t know what data science is and what problems it can solve.
Luckily, there are new tools available today that can help bring the data science and insurance teams together.
“Automation tools like DataRobot empower capable and motivated non-data scientists to take on some of that work,” explained Surrette, adding that this is a big change from the past, where insurance professionals would have to turn to online courses to learn about leveraging data.
“What these new tools require of the user is you have to be able to identify a business problem and you have to know some of the basic things that the tool requires to build models,” continued Surrette. “The user needs to know what the tool needs to build these models, and what the tool requires is that the user know how to collect the data and set it up in an appropriate way for what they’re trying to model.”
For example, if an insurance professional is trying to make predictions about specific policies, then they need to give the model a dataset that consists of those policies. If they want to predict something about claims in the future, then they need to provide the model with examples of claims from the past. Once a user is able to identify and feed in that data, the tool takes care of the rest, while helping the user avoid common pitfalls, like overfitting data.
The immediate benefit of putting data science capabilities into the hands of insurance professionals is that they can prioritize and work on projects that require data analysis, without having to hand them off to the data team within the organization, and wait their turn for the project to be completed.
“Beyond that, another benefit is that now that you have people who see both sides – they understand the problem for the business and they understand the solution in the data science,” said Surrette. “In turn, they’re able to uncover and identify new situations to which they can apply machine learning and data science, ultimately saving the company money, and improving efficiency and customer experience.”
Source: Insurance Business Magazine
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