The U.S. health care system loses more than $100 billion a year to fraud, waste, and abuse, and by some estimates several times that. We have known this for decades. What is new is that the technology to catch it has finally caught up. The question is no longer whether the tools work; it is whether the institutions processing and paying the faulty claims will finally use them.
What the system is actually losing
Three different problems get lumped together, so it is worth separating them.
- Fraud is intentional deception: billing for services never rendered, phantom patients, kickback rings. The National Health Care Anti-Fraud Association estimates fraud alone at 3 percent of health spending on the low end, and as high as 10 percent. Against $4.9 trillion in national health spending, even the low number is roughly $150 billion a year.
- Waste is overutilization: unnecessary tests, redundant procedures, services that raise cost without improving care.
- Abuse sits in between. It is improper billing, like upcoding and unbundling, that inflates payments but may not meet the bar for criminal fraud.
In 2024, the Justice Department recovered about $1.7 billion from health care fraud. Set that against $150 billion in losses and the system gets back roughly a penny on the dollar.
Why The old playbook fails
This low recovery rate is structural. Health plans pay first and investigate later. Only after a claim is submitted, adjudicated, and paid – a process spanning days if not weeks – does anyone check whether it was legitimate. Two tools currently dominate that after-the-fact review, and both share the same flaw:
- Rules engines flag claims that match a known pattern, like a dermatologist billing 200 procedures in a day. They only catch schemes someone already thought to define, so they are always one step behind.
- Retrospective audits sample claims that already cleared. By the time an auditor builds a case, the provider has often billed millions more, moved, or shut down.
Both also produce huge volumes of false positives. Investigators end up spending most of their time clearing legitimate claims instead of chasing real fraud.
What AI does differently
Novel AI techniques flip this approach altogether. Instead of matching known patterns, AI learns what normal looks like and flags what deviates.
Three critical AI capabilities matter most:
- Anomaly detection. Models learn normal billing for a given specialty, region, and patient mix, then surface the outliers: the provider whose coding intensity is far above peers, the utilization curve bending the wrong way. A 2025 review in Artificial Intelligence in Medicine found the hard part is no longer spotting anomalies. It is doing so when fraud is a tiny fraction of the data and investigators need to trust the result.
- Network analysis. The expensive schemes are rings, not individuals: a physician steering patients to a lab he secretly owns, a pharmacy filling scripts from a complicit prescriber. Graph models map the relationships across providers, patients, and facilities, and expose clusters that no claim-by-claim review would ever catch.
- Document review. Language models can read the clinical note and compare it against the billed code. That is where waste and abuse live: documentation that does not support the level of service charged.
The capability that actually changes the economics is prepayment scoring: running claims through these models before they are paid, so the suspicious ones are held or reviewed first. That turns the whole problem from recovery into prevention. Instead of chasing a fraudster after the fact, you never pay him at all.
Where the opportunity lies ahead
The winning startups here will not be the companies selling slightly better audits after the money is gone. They will be the companies that move detection into the payment pipeline, and, harder still, the ones that connect data across multiple payers. Cross-payer data will allow these companies to build continually improving agentic systems that detect fraud incrementally better every day.
The final step required here is mass adoption: getting risk-averse institutions like large payers to act on a probabilistic flag in a setting where a wrong accusation carries real consequences, and getting them to share data they have every incentive to hoard. That is a far easier problem than inventing the technology, and we have already done the hard part. The only open question is how long the healthcare industry will keep treating a hundred-billion-dollar leak as the cost of doing business.
About Kasra Khadem
Kasra Khadem is a Partner at Pathlight Ventures, an early-stage venture capital firm in New York City, where he leads healthcare technology investing. Prior to joining Pathlight, Kasra was an Investor at Human Capital where he partnered with leading healthcare startups like Commure, Transcarent, and Ambience Healthcare.
