How to boost healthcare fraud detection accuracy while minimizing false positives
The healthcare industry loses an estimated $430 billion annually to increasingly complex fraud, waste and abuse. Only 10 to 15 percent is ever recovered.
With artificial intelligence, anomalous billings can be flagged or auto-denied, enabling investigators to focus on high-value, complex cases.
What if you could continuously monitor provider behavior and risk levels, manage daily billing fraud risk in real time and access pre‐pay analytics to identify FWA before claims are paid? AI for healthcare fraud detection can do all this and more.
Read this e-book to learn:
With artificial intelligence, anomalous billings can be flagged or auto-denied, enabling investigators to focus on high-value, complex cases.
What if you could continuously monitor provider behavior and risk levels, manage daily billing fraud risk in real time and access pre‐pay analytics to identify FWA before claims are paid? AI for healthcare fraud detection can do all this and more.
Read this e-book to learn:
- How AI optimizes workflows by triaging good, bad and suspicious claims
- Why higher detection rates and fewer false positives helps teams focus on more complex FWA schemes, resulting in substantial savings
- How models are continuously trained, evolving with schemes as they arise
Please fill out the form to download the whitepaper.