With FWA losses of $170 B a year, US healthcare insurers look to AI for better solutions.

US healthcare is a $3.4 trillion business. Fraud, waste and abuse (FWA) costs payers (government and commercial health plans) $170 billion a year in the U.S., representing three to six percent of all payments.

Insurers spend $2–5 billion annually on solutions to mitigate FWA in the U.S., but only catch less than 10 percent of fraudsters.

The Government Accountability Office (GAO) has deemed Medicaid to be highly vulnerable and fraud schemes are becoming increasingly complex, (e.g., phantom providers billing false claims from an empty storefront or nonexistent office). Often these schemes go undetected by current technology solutions or time-intensive manual investigations.

There is growing recognition that preventing and stopping fraud before a claim is paid (pre-payment) is more effective than “paying and chasing,” meaning paying the provider and then attempting to recover the overpayment.

Compounding the problem is that fraudsters’ methods evolve so quickly, it is difficult to stay ahead of them.

A much more efficient approach to detecting and preventing FWA is to use artificial intelligence (AI) to create prospective (pre-pay) models built by identifying anomalies found in historical claims data (post-pay).The pre-payment model identifies FWA before the payer sends good money for bad claims, significantly reducing the need for inefficient pay-and-chase activities.

Brighterion AI enables live updates to existing models based on real-time claims experience, reducing “digital noise” and automating some of the manual processes involved in maintaining and updating existing rule-based technology.

This enables automated self-learning and adaptation in real time with the ability to identify existing and emerging fraud patterns and schemes, both simple and complex.

Using AI Express, we build those models to be production-ready in 6-8 weeks, unlike our competitors who require months (and sometimes years) to get you up and running. By using your historical data, we can ensure immediate savings. This and future data are received from any source, in any format, and our proprietary Smart Agent technology enriches it using AI and machine learning. Unsupervised and supervised learning improve outcomes over time, creating pre-pay AI models that grow and mature with the everchanging behaviors of fraudsters.

Brighterion AI increases fraud identification, reduces false positives and stops fraud before payments are made.

What do you prefer: to pay and chase, or use proactive FWA prevention and detection? We thought so. We’ll have your back.

AI for Fraud Prevention in Healthcare
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