ATS Underwriting Chooses Gradient AI to Expand Medical Stop Loss Insurance Business
Gradient AI’s SAIL Removes Barriers for Small Groups in Transition to
Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, today announced that ATS Underwriting, a privately held, full-service managing general underwriter (MGU) specializing in Medical Stop Loss insurance, has adopted Gradient AI’s SAIL™ Solution to enhance its underwriting processes within the small group transitional market. This market primarily consists of smaller companies undergoing a significant shift from fully insured to self-funded plans.
ATS Underwriting specializes in delivering affordable medical stop loss insurance solutions. With a 30-year track record in underwriting and working with AM Best rated “A” (excellent) carriers, the company is dedicated to meeting the evolving needs of its clients. Recognizing the importance of expanding self-funded opportunities for its small business and carriers, ATS determined that AI technology would best support this strategic goal.
After using a competitive AI solution, ATS switched to Gradient AI’s SAIL Solution due to its high-quality medical industry data lake, powered by AI predictive analytics. By harnessing the advanced features of SAIL, ATS’s team has gained valuable insights and increased confidence in its underwriting decision-making. As a result, the company can deliver greater service to its clients in a challenging yet growing market.
“As a leader in the small group transitional market, partnering with Gradient AI is helping ATS leverage a massive opportunity in the stop loss space and establish a stronger presence in the market,” said
Traditionally, underwriting for small groups requires the use of individual health questionnaires (IHQs), meaning employees must provide detailed information about themselves and their dependents. This process can be burdensome for employees, challenging for HR departments, and administratively complex for MGUs. Consequently, most smaller groups and MGUs forego IHQs, missing out on potential self-funded opportunities that may otherwise have been available to them.
However, when using a medical underwriting AI tool like SAIL, IHQs are no longer needed to predict health plan risk. This removes a significant barrier for smaller groups that want to self-fund, granting them access to cost savings, flexibility, and benefits usually reserved for larger groups.
Since 2007, ATS estimates that the number of companies with three to 500 employees buying self-funded medical coverage has grown by over 50%. As market pressure continues to push for the elimination of IHQs, predictive analytics enables insurers to secure quotes earlier and provides greater flexibility for stop loss carriers that cover companies of this size. Similarly, for carriers that cover larger companies, the use of predictive analytics also helps streamline underwriting and accelerate quote turnaround times.
“Until now, lack of medical claims data represented a barrier to entry to the small business self-funded market for medical stop loss providers,” said
Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprising tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic, and demographic information. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. By using Gradient AI's solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com.
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Source: Gradient AI


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