70% of claims received are flagged as unusual—i.e., as potentially incorrect—based on the health insurer's specific rule book. Administrative staff then check these claims in detail. Based on the claim information and any available patient history data, the staff then draw on their experience to decide whether or not to intervene
Unprecedented value of using machine learning in healthcare insurance claim auditing
Robotic process automation (RPA), robotic desktop automation (RDA), and cognitive document processing (CDP) can completely transform the claims audit process to improve productivity and efficiency while decreasing costs.
The AI based efficiency improvements deliver measurable impact. The savings currently achieved from successful claims reductions are ~3% of the amount invoiced. An increase of ~1% would save a typical insurers an additional of $588 million yearly
Optimization of HR
Automated prioritization empowers the administrative staff not to longer have to check every claim deemed unusual, but can instead focus on those cases that have the greatest reduction potential and the best prospects for successful intervention
The benchmarking test results of AI based auditing algorithms show that the hit rate is closely approximates the ideal value of human auditor. That means, the AI based system correctly filters out almost all claims where the claim amount could be reduced
The AI system not only simplifies and accelerates the overall claims management procedure, it also enhances its quality: additional costs for redundant audit and rejection processes are eliminated, while available resources can be focused on the "right" cases