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FraudScope’s $7 Mn Series A to Expand AI Solutions in Healthcare Fraud

Leading venture capital funds QED Investors and Brewer Lane Ventures join FraudScope’s list of prominent financiers

FraudScope, the fast-growing AI-based platform that helps health plans address the increasing problem of fraud, waste, and abuse, today announced it has completed a $7 million round of Series A financing. The largest participants in the round were QED Investors, Brewer Lane Ventures, and the GRA Venture Fund.

“QED is singularly focused on supporting companies that solve real-world problems and can have a broad societal impact. The rising cost of health care is a perfect example of an issue that hurts consumers, businesses, and the economy as a whole – and one that technology is uniquely positioned to help solve,” said Matt Risley, Partner at QED Investors. “We look forward to using our operational expertise to help the FraudScope team use its cutting-edge machine learning to reduce healthcare fraud, shoring up health plans and bringing down costs for consumers and employers at a time when both are under tremendous pressure.”

“Our artificial intelligence-driven innovations in fraud, waste, and abuse identification and prevention are matched only by the results we deliver to our customers,” stated Musheer Ahmed, CEO of FraudScope. “With this round of financing, we are closer to realizing our vision of becoming the AI-based collaboration platform that integrates across fraud, payment integrity, clinical and provider network teams to drive down costs and increase efficiencies for health plans.” Traditional rules-based techniques to address healthcare fraud, waste and abuse are reactive and cannot identify new and emerging schemes in a timely manner leading to significant financial losses at health plans. FraudScope’s solutions are designed to proactively monitor and identify new schemes as soon as they emerge which helps maximize savings.

The additional capital will be used to scale the team and expand product offerings to meet the growing needs and opportunities at health plans. “Fraudscope is a leader in AI driven health fraud detection,” said John Kim, Founder and Managing Partner of Brewer Lane Ventures. “We are delighted to leverage our operating experience in the insurance industry to assist the company with its strategy and marketing efforts.” This Series A Round brings FraudScope’s total funding to $10.5 million. Existing investors that also participated in the round include Spider Capital, Mosley Ventures, and TechSquare Labs.

The National Health Care Anti-Fraud Association and other government and law enforcement groups place the cost of healthcare fraud at 3 percent to 10 percent of the total healthcare costs in the U.S., or as much as $300 billion. FraudScope provides health plans with advanced identification and workflow tools to detect and investigate emerging fraud, waste, and abuse schemes, and prioritize claims to greatly reduce their financial risk. These capabilities become extremely valuable to health plans, especially during the COVID-19 pandemic due to the increase in new fraud, waste, and abuse.

This announcement follows a host of other news coming from FraudScope in recent months. In the first quarter, FraudScope announced its AI platform had helped health plan customers realize tens of millions of dollars in fraud, waste, and abuse identifications and savings. FraudScope also recently announced that it achieved Certified status for information security by HITRUST®, and the company rolled out a series of upgrades and enhancements for its proprietary case management and reporting technology.

“As an early investor, the GRA Venture Fund is pleased to see the continued growth of FraudScope’s technology and customer satisfaction,” said Kurt Jacobus Managing Director of the GRA Venture Fund. “Fraud, waste, and abuse are affecting the whole healthcare system, and we believe that FraudScope’s AI is a strong market differentiator. ” Information about FraudScope’s AI platform and claims investigation solutions can be found at

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