Research collaboration with American Family Insurance highlights UW’s ability to tackle business challenges.

“Big data” and “analytics” have overturned fields ranging from astrophysics to online retail. Now, American Family Insurance, a major multi-line insurer with 4,800 employees in Wisconsin, is seeing results from a formal research collaborative with the University of Wisconsin-Madison to apply big data analytics to the company’s datastream.
“We want to use data to create new opportunities across the business, including service and pricing, to bring additional value to customers,” says Justin Cruz, vice-president of strategic data and analytics at American Family.
The collaboration draws on graduate students and professors from the Wisconsin School of Business, the Department of Computer Science, the Department of Electrical and Computer Engineering, the Department of Statistics, and other units with expertise in data science.
Data within the insurance industry offers plenty of opportunities, says Cruz. “One fundamental requirement when you write a policy is to understand the risk, and there’s always a maze of factors that affect risk.” For an auto policy, to determine how likely the applicant is to have a claim, the insurer looks at factors such as driving record, age, region and type of car. Better ability to explore the “granular” data in the company’s files should lead to more precise measurements of risk, and therefore more accurate pricing.
Projects at UW–Madison with American Family funding include:
- Drone images – As the company ponders expanded use of drones to assess storm damage, its wants to defocus faces in photos to prevent invasion of privacy. The partnership includes computer science professor Vikas Singh.
- Time patterns: Data on work flow can streamline operations and ensure customer satisfaction, Cruz says. “Before we cut a check to settle a claim, there can be many steps, including phone calls, visits to the claim center and decisions by the adjustor.” With a smarter analysis of work flow, “We can predict customer satisfaction and quickly correct obstacles.” Business professor Neeraj Arora assisted with these efforts.
- Matching: American Family wants to recognize customers across all systems to ensure they receive appropriate discounts or services. “Data can be noisy,” Cruz says. “Names are misspelled, memories fade and addresses change, so making these matches can be difficult and costly.” Working with AnHai Doan, a professor of computer science, and his grad students, “we have built and deployed a machine-learning algorithm all across our businesses that has already saved us significant money.”


