Cotiviti launches AI-powered coordination of benefits solution for payers

Cotiviti launched Proactive COB, an AI-powered tool that detects coordination of benefits errors at enrollment instead of after claims are filed, targeting costly misrouted claims. The Salt Lake City company is piloting the product now, with general availability planned for Q1 next year.

Categorized in: AI News Healthcare
Published on: Aug 14, 2026
Cotiviti launches AI-powered coordination of benefits solution for payers

Cotiviti launched a new AI-powered coordination of benefits solution on Thursday to help health plans identify when members have multiple insurance policies. The Salt Lake City-based healthcare infrastructure company is piloting the product, called Proactive COB, with plans for general availability in the first quarter of next year.

Coordination of benefits is the process insurers use to determine how medical expenses are covered when a person has more than one health plan. Errors typically occur when a member's coverage changes due to life events like switching jobs, retiring, getting married or having a baby. That's when the order of which plan pays first can shift, and the data required to make that determination is fragmented across insurers.

"Any of those changes can change the order of the benefits," said Matthew Herbein, vice president of product at Cotiviti, in an interview. "What that means is, if you go to a healthcare provider and you have multiple health insurances, well, that provider has to kind of understand which one to bill as primary, and then which one to maybe bill as secondary to cover your member responsibility. The issue at hand is that data is very fragmented."

When people enroll in new coverage, health plans often don't receive complete or accurate information about their other policies. Errors can go undetected until after claims are paid, which leads to costly recovery efforts and additional administrative work.

How Proactive COB works

Proactive COB identifies and validates when a member has other health insurance and determines the correct order in which benefits should be paid. It relies on member enrollment data rather than waiting for a medical claim to be submitted to spot coordination of benefits issues.

"Today, coordination of benefits is done once a claim comes in, once you or a family member goes to the doctor," Herbein said. "[Proactive COB is] actually a claim-agnostic coordination of benefits capability that identifies other health insurance and correctly identifies the correct order of benefits at enrollment. Nobody else in the industry is actually doing it as early as enrollment."

Cotiviti offers solutions aimed at increasing transparency and collaboration between payers and providers, including products related to payment accuracy and fraud detection. Its new tool extends that work into the enrollment process, which is an area that relates directly to improving how insurers handle administrative tasks, a key focus for professionals exploring AI for Healthcare in their organizations.

Measuring success in the pilot phase

To track how well Proactive COB works during the pilot, Cotiviti is focused on cost avoidance - reducing administrative waste from misrouted claims. The company is also monitoring how often the system correctly changes the order of benefits, the per-member-per-year impact, and how smoothly the solution integrates with health plans, Herbein said.

Why this matters for healthcare professionals

The problem Proactive COB targets is familiar to payer operations teams: misrouted claims generate reconciliation work, payment delays and member complaints. If Cotiviti's approach catches secondary coverage earlier in the lifecycle, it could lower the administrative load on both payers and billing staff at provider clinics. Healthcare professionals who manage enrollment workflows will want to watch the pilot results for evidence that coordination of benefits errors can be corrected before a claim is ever submitted - and whether that correction holds up across a full plan year.


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