Data Wears the Crown for Insurance Actuaries
Robert Regis Hyle | August 19, 2014
Data is king at Glatfelter Insurance Group because, from an actuarial perspective, unless there is good, clean data and easy access to that data, the job of the insurer’s actuaries is much more difficult, according to Jim Partridge, senior vice president, operations, and chief actuary for Glatfelter Insurance Group.
Partridge works closely with Glatfelter’s IT department and CIO Larissa Tosch to ensure that any new insurance program or products that are rewritten—which could affect what the actuaries do or affect the data passed from the insurer’s front-end systems—the actuarial department is involved in the discussion to make sure all the data elements are coded correctly.
The actuarial department uses data in two ways, according to Partridge. With Glatfelter’s wholesale programs, the actuaries are responsible for creating, programming, maintaining and distributing what the insurer calls its corporate monitoring reports. The actuaries maintain reports for senior management on key statistics ranging from account retention and production, to loss experience.
The second area of responsibility for the actuaries is to be sure any rates being charged to policyholders are adequate at every level, which means deep analysis of the company’s experience.
“In order to do that appropriately we have to have good data,” says Partridge. “When I came here, the company had just finished building a data warehouse. It’s been tweaked and restructured since then, but it works well and the data we have is clean.”
A year ago, Glatfelter decided to create a corporate reports governance committee. Even though the focus of the governance committee is reports, Partridge explains that data governance has also fallen under the direction of this committee.
“In order to build reports that need to be beneficial you have to have good data governance,” he says.
Partridge has a team leader that reports to him and is responsible for overseeing the reports, meets monthly with the head of the data warehouse team, and makes sure any issues with data that comes through the data warehouse has oversight.
“As we find problems, we are on the same page on how things should be coded,” he says.
The actuarial department at Glatfelter is not technically responsible for auditing the data, but Partridge points out actuaries have a professional, ethical obligation to test the reasonableness of all data. When issues arise or some of the data doesn’t appear to make sense, the Glatfelter actuaries try to identify the problem to make sure it’s not an error they made. For the most part, though, the company relies on IT to build and maintain the data warehouse, even though the actuaries do most of the querying and programming themselves.
“We’ll drill down and identify the data issue and we’ll look at the data for particular policies on our front end system to see if there is an issue where the data is not interfacing correctly from the front end to the back end,” says Partridge. “We try to do our own analysis and if we are able to discover the issue we’ll have dialogue with the IT department. If it is pretty clear, we’ll submit a programming change request so IT can correct it. If we don’t know where the problem is coming from we send our information to IT and have them do the investigative work and we’ll both work from there to get it corrected.”
Amount of data
The biggest change Partridge has found with new forms of data is some current data comes from the company’s billing system and goes into the data warehouse. In the past, most of the data the insurer was using came from the policy and claims systems.
There have been issues with some other data sources, explains Partridge, where a need was identified and data would be dumped into the data warehouse without the involvement of the actuarial department in the process.
“Over time, when we tried to use that data it was hard to use in its current form and we went through a process of trying to revive the structure of the data so it integrated with the existing data,” he says. “We still largely rely on the policy and claims systems data so it’s been more an issue of a larger volume of data and having a data warehouse system to get better response time and do faster queries. We constantly try to upgrade to improve the speed.”
Three years ago, Glatfelter rebuilt the data warehouse and identified issues with the structure of some data, explains Partridge. The way the data warehouse was previously structured was not the best structure because the queries took too long to run. Since then, the insurer has worked to improve the structure in addition to building data cubes so analysts don’t have to query against the whole data set when they try to find something.
Glatfelter uses industry data to fill in where they don’t have credible data, but most of that data comes from industry sources such as ISO or NCCI and is more of an aggregate or trend data.
“We haven’t gotten into heavy external data partly because our business in the wholesale program specter is so niche that there’s not a lot of data out there that is relevant,” says Partridge. “A generalist insurance company that insures everything from soup to nuts might need more, but we’re focused on a wholesale niche program. We’re the industry leader in that field and we have the most data of anybody, so it’s hard to fill in with external data.”
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