EvolutionIQ unveils its AI-driven tech to reduce the cost of insurance claims

Processing bodily injury claims is not just a matter of crunching numbers and evaluating a transcript of events. Because a particular claim may be open for 10 years and have hundreds of data points and medical notes, adjusters must be de facto medical experts to make informed decisions.

“It’s not like transactional data in a spreadsheet,” said Tomas Vykruta, CEO of claims guidance platform company EvolutionIQ. “Bodily injury claims can be like a medical book to read.”

Insurance is a massive industry — it’s worth an estimated $1.3 trillion in the US alone, and insurance companies pay an estimated $230 billion annually in claim payouts on disability, workers’ compensation, and complex business accident lines alone. .

But for the benefit of insurance companies, adjusters and the general public, that amount can, and should, be drastically reduced, according to Vykruta. To this end, EvolutionIQ has developed what it calls the first-in-the-circuit human artificial intelligence (AI) claims guidance technology for the insurance industry.

EvolutionIQ leads with ML

The three-year-old company, which today announced a $21 million Series A funding round, built its system based on deep learning, an advanced branch of machine learning (ML). This leverages AI recurrent neural networks (RNNs), which are based on time-series data or sequencing-related data.

This allows the system to actively monitor all open group and individual short-term and long-term disability, workers’ compensation and property and casualty claims under an examiner’s control to guide them toward those that require further attention, further action or action. complex decisions. It will generate a list of the few that are most actionable, along with a “deep explanation” of why and what outcome they should be aiming for.

With respect to bodily injury, the system can read a full sequence of events that describe the claim and relies on RNN data to simulate injury sequences, comorbidities (the presence of multiple diseases or medical conditions), demographics, conditions, and others. factors. These can provide projections as to when a claimant might recover, to what extent they might recover, and to what working conditions and responsibilities they might return.

For example, with a short-term disability claim, a worker may have been off work for a week and will be in a queue with several similar workers. The system will pick up on that and determine that they could go back to work within, say, 45 days, provided they receive some vocational training.

“It will put the information right in front of the examiner,” Vykruta said. “It’s a crystal ball where they can see, ‘This is where you should spend your time.'”

As he pointed out, adjusters can be overwhelmed by dozens of complex claims that can last for years and are often worth hundreds of thousands of dollars each. These can be documented in hundreds of disparate pages and in many structured and unstructured formats.

Also, “these are impossibly complex issues because there are bodily injuries,” he said. “You have to be a doctor in many cases to understand cases of comorbidities. There are too many complex problems and too few people to be able to analyze them”.

That said, the human-in-the-loop deep learning AI system must have people connected, he said. Examiners are not eliminated; rather, they contribute to the system as it constantly learns, evolves, and recalibrates itself based on new data and events. “Dealing with bodily injury is a really complex task and a huge data problem,” Vykruta said. “The system has to partner with human experts.”

Modernization of insurance claims management

Working with clients like Reliance Standard, Principal and Sun Life, EvolutionIQ has processed millions of claims. Operators and third-party administrators using its software for more than a year have seen reductions in claims flow of up to 45%, according to Vykruta, and the incidence rate of workers going from short-term disability to long-term disability. in the long term it has been reduced by approximately 50%.

“Claim management is ripe for modernization,” said Vykruta, a former AI technical lead at Google. “It is the biggest operational problem that affects operators because it is a huge human effort that can be greatly improved using data. Tens of thousands of claims are open at any given time and there is a great opportunity to impact them now with the right information.”

Vykruta explained that the funding will be invested in R&D and the development of new AI modules. You’ll also help develop the company’s team of engineers, data scientists, and product and customer service experts. The company currently has 45 employees, many of them from Google, Facebook, Amazon and Bloomberg, and plans to grow that base to 85 by the end of the year. Vykruta noted that more than 25% of EvolutionIQ’s technical employees come from Google, a rarity for companies in the insurance industry.

As he pointed out, insurance is a huge and necessary industry for the modern world, but he also underscored the fact that EvolutionIQ’s long-term goal is to reduce the industry’s operating costs and premiums, benefiting everyone from companies to adjusters and claimants and policyholders alike.

“We are very focused on making the claims process more efficient and affordable for everyone,” he said. “We consider this to be the inevitable future. In the next five years, every operator will have to have a system like this, or they won’t be able to keep up.”

The EvolutionIQ Series A round was led by Brewer Lane Ventures. Seed investors FirstRound Capital, FirstMark Capital and Foundation Capital also participated, along with Altai Ventures, Asymmetric Ventures, Reliance Standard Life, New York Life Ventures, Guardian Life and Sedgwick.

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