Marlabs automates 85% of healthcare payments for one provider
Marlabs developed an AI system to automate payment posting for a healthcare billing company handling 85,000 monthly payments. The system uses large language models to interpret payer rules—even undocumented ones—and posts payments automatically, reducing errors by 95% and freeing staff for complex cases. Claims still require human review, but the AI flags exceptions with explanations, speeding…
Key points
- AI system automates 85% of 85,000 monthly payments for a healthcare billing company
- Reduces posting errors by 95% and speeds denial resolution with human oversight
- AI uncovers undocumented payer rules by learning from real outcomes, not just rulebooks
The company processed payments faster, resolved denials more efficiently, and gained real-time visibility into exceptions. Marlabs emphasizes starting with high-volume, rule-heavy tasks like payment posting, using AI to uncover implicit rules and ensure transparency in every decision. The approach aims to scale operations without adding headcount, directly improving cash collection for providers.
How AI-Powered Automation Can Solve the Payment-Posting Bottleneck
Unite.AI · 5 November 2026
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This text was published by Unite.AI and written by Brian Rowe, Senior VP of AI & Machine Learning, Marlabs. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
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