NANO FWA
AI-powered Fraud, Waste and Abuse Management Solution
Catch it before the payment leaves, not after
Recovering a fraudulent payment means persuading someone with no incentive to cooperate to give the money back, months later, usually for a fraction of it. Preventing the same payment costs a review. That gap is the entire business case for payment integrity, and it is why NANO FWA sits before adjudication rather than after it.
The low-code automation platform brings the whole picture of a suspect claim into one view — history, provider pattern, member pattern and the rules it broke — so an investigator opens a case already knowing what they are looking at.
What NANO FWA gives a payer
- Fewer claim errors reaching adjudication
- Lower cost and less manual intervention per claim
- Cost-containment targets that are met rather than reported
- Payment integrity handled as a system, not a recovery exercise
- Fraud, waste and abuse caught before the claim is paid
- Higher auto-adjudication rates, with payment accuracy to match
- Compliance you can evidence, on demand, per claim
- Shorter investigations, because the case arrives assembled
The hard part was never recognising fraud once you are looking at it. It is the volume: finding the few claims worth a human hour inside millions that all look ordinary. That is a machine problem, and it is the one this platform solves first.
The detection models were designed with investigators rather than for them, which is why the output is a case an investigator can work rather than a score they have to interpret. NANO BRAIN — trained on hundreds of millions of processed claims and approvals — flags fraud, waste and abuse as part of the same automatic pass.
Case management efficiency
up by around 58%
in NANO deployments
Regulatory compliance,
evidenced
Return on investment
of up to 25:1
in NANO deployments
Faster investigation, fewer dead ends
A unified view of each FWA case gives payers a single place to triage, investigate and document — with the evidence attached to the case rather than scattered across inboxes.
The same models surface emerging patterns, not just known ones: schemes change faster than rule sets, and a system that only catches what it was told about last year is a system that catches last year.
Industry estimates put losses to fraud, waste and abuse at around 12% of annual healthcare spend. Whatever the true figure in your book, it is large enough that a percentage point of it pays for the programme.
See what a percentage point of your loss ratio is worth
Related products
Products in the suite that work alongside this one.
More about this product
How this works with the rest of the suite
- NANO BRAIN
BRAIN is the model layer underneath — its training on processed claims and approvals is what makes the flag worth an investigator hour.
- NANO CSS
The scrubber stops a provider submitting a wrong claim; payment integrity stops a payer paying a wrong one. Opposite ends of the same transaction.
- NANO MAS
Auditing samples and reviews after the fact; FWA is the pass that runs on every claim before the money moves.
- NANO PBM
Pharmacy benefit is where a large share of payer leakage lives, so the same detection has to reach the drug claim as well as the medical one.
Frequently asked questions
What is fraud, waste and abuse in healthcare claims?
- Three different problems usually handled together. Fraud is deliberate misrepresentation for payment. Waste is spending that produces no benefit. Abuse is practice that is not deliberate deception but still charges a payer for what it should not. They matter together because they are detected the same way, in the same claim stream.
Why act before payment rather than recovering afterwards?
- Recovering a fraudulent payment means persuading someone with no incentive to cooperate to give the money back, months later, usually for a fraction of it. Preventing the same payment costs a review. That gap is the entire business case for payment integrity, and it is why this sits before adjudication.
Was the hard part not recognising fraud?
- No — recognising it once you are looking at it is comparatively easy. The hard part is volume: finding the few claims worth a human hour inside millions that all look ordinary. That is a machine problem, and it is the one the platform solves first.
Does it only catch schemes we already know about?
- The models surface emerging patterns as well as known ones. That distinction matters because schemes change faster than rule sets do, and a system that can only recognise what has already been written down is always describing the previous year.
What results have payers seen?
- Case management efficiency up by around 58% in NANO deployments, and a return on investment of up to 25:1 in NANO deployments. Alongside those, fewer claim errors reaching adjudication, higher auto-adjudication rates with payment accuracy to match, and compliance that can be evidenced on demand, per claim.