ACM has built a new digital tool, SubroFy, to significantly increase the identification of subrogation opportunities. The subrogation tool is a user-friendly platform fits into the claims team’s daily workflow in an efficient, focused way, ensuring that it will make a real impact.
“In the claims industry, subrogation is one of the areas that has the highest amount of missed opportunities,” said Dhara Patel, ACM president. “There’s a lot of hidden subrogation potential that may not be obvious to even the most experienced adjuster.
“I think this will be huge, especially for those that either outsource their claims or even have their own claims department. I honestly don’t know of another subrogation tool like this out there,” she added.
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“While we had used traditional subrogation triggers in the past, we needed to develop a more sophisticated approach to data to find opportunities that wouldn’t otherwise be identified,” explained Brock Howard, senior vice president.
How ACM’s subrogation tool works
Our subrogation tool uses an advanced machine learning algorithm tool, coupled with NLP – natural language processing. A big part of identifying subrogation opportunities is the loss description from the adjuster’s notes. NLP takes that loss description from either the adjuster or CSR and breaks apart the text into individual words. In conjunction with National Programs’ Data Science & Analytics team, they identified a set of keywords that the tool searches for, based on historically successful subrogation recoveries. The subrogation tool then extracts those keywords from a new claim to point out subrogation opportunities.
“We’re taking specific aspects of the claim and unstructured, free form text to assist in recovering claim dollars for our carrier partners,” said Jason Perone, data scientist.
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“Subrogation opportunities like this occur very frequently. It’s why we think there’s a lot of hidden opportunities, because so much of the information that the adjusters are collecting is just sitting, unused, in free form text on their tablets,” Patel explained.
In the future, Patel says, they’ll be able to look at a carrier’s loss history along with their loss descriptions and notes, and then run a model to see if there are opportunities for savings that they’ve not seen. In other words, ACM is able to provide carrier partners with the claims that have a definite potential to subrogate.
Some carriers are really, really savvy about subrogation, with a large, dedicated department and a large budget. Other carriers don’t invest their dollars in subrogation, she explained. “Everyone wants subrogation, but few actually build subrogation into their modeling, and I think it simply gets overlooked quite a bit,” she said.
How the platform was built to maximize subrogation opportunities
The Data Science team began by pulling data available in ACM’s databases, prepared it for modeling and built a machine learning model. After discussing with ACM as to how they needed the tool to be implemented, they created a process to automatically pull in new claims every single day and grade them in terms of high, medium or low subrogation chance.
To handle claims that were predicted to have high likelihood of subrogation opportunities, they built a web application that ACM’s claims team could review daily as part of their claims review workflow. The claims team also receives a daily email with new claims that rate high for subrogation possibilities. Just as important, the team is now able to weed out most of the false positives so they can focus in on where the greatest possibilities lie.
“We’re excited to be able to use data in innovative ways to make more intelligent decisions – in a more automated fashion – producing positive results that favorably impact an insurer’s bottom line,” Howard said. “We’re not just looking at data. We’re getting into the why of the subrogation: the potential for how it impacts the claim paid and therefore the company’s bottom line.”
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“These subrogation opportunities are hidden-value impact areas to a loss ratio. An actuary may build in a certain amount of subrogation, but often there’s not enough of an emphasis, versus how powerful a subrogation can be on a loss ratio,” Patel said. “In a way, it’s found money, right?”
She added, “We embarked on a powerful partnership with our internal Data Science team using machine learning to create a unique subrogation model. We believe our subrogation tool is going to produce unexpectedly positive results on loss ratios for all of our clients going forward, taking our claims services to the next level.”