InsurTech Verikai launches product to offer “holistic view of risk”

Insurance technology startup, Verikai, has launched a product, Med/Rx, to give insurers robust insights on medical claims and prescription data.

Founded in 2018, Verikai uses machine learning models to give insurers a deeper view of risk, using extensive clinical and behavioural data to create risk scores.

Now, with the addition of Med/Rx, the InsurTech said that carriers will be able to run group reports through Verikai’s platform to establish a baseline of high-cost conditions and prescriptions that appear in each group.

The Med/Rx report includes 10,000 of the most expensive medical diagnoses, including cancers, chronic illnesses, mental disorders, injuries, and drug abuse, to name a few.

The Rx half of the report provides all known prescription data on a group, broken down by pricing category (generic, brand, and specialty drugs). As Med/Rx takes shape, Verikai said it will continue to update the product with additional medical conditions and deeper insights into price.

Verikai’s President and CEO, Jeff Chen, said, “Our mission is to utilise our unique dataset and machine learning technology to provide the insurance industry with the best insight, so underwriters can make the most accurate decisions possible around evaluating risk. Med/Rx will offer our health insurance customers historical claims, medical, and Rx data – on-demand, within minutes.

“We have been working on this product for over a year and coupled with our risk scores from our flagship Capture product, underwriters will have the most powerful set of predictive risk tools at their fingertips, all within a single platform. There is no other predictive risk vendor on the market that offers carriers a complete and holistic view of risk like Verikai does. We look forward to the positive impact Med/Rx will have on the industry for years to come.”

Earlier this month, Counterpart, an AI-driven management liability InsurTech for small businesses, launched a crime insurance product.

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