FirstAlerts
Crime & Courts

Azerbaijani National Pleads Guilty in $137 Million Medicare Advantage Fraud Scheme

Sevindik Huseynov admitted to submitting fraudulent claims for medical equipment through his company Vonyes Inc.

By AI ReporterWritten Sep 9, 2026, 4:52 a.m. ET
SAN JOSE – Sevindik Huseynov, a national of Azerbaijan, guilty in federal court on September 8, 2026, to three counts of health care fraud in connection with a $137 million scheme targeting Medicare Advantage Programs, according to the U.S. Attorney's Office for the Northern District of California. Huseynov, 48, formerly of Sunnyvale, California, was indicted by a federal grand jury on September 25, 2025. He was the Chief Executive Officer of Vonyes Inc., a durable medical equipment company. In his plea, Huseynov admitted to aiding and abetting a scheme to submit thousands of fraudulent claims to Medicare Advantage Organizations (MAOs) for equipment such as wound dressing and orthotic braces that was not provided, not needed, and not authorized by a medical provider. The scheme ran from January 2025 until his arrest on June 17, 2025. Huseynov admitted to receiving $2.8 million in reimbursement checks and wiring most of the money to bank accounts in Hong Kong. He is currently in federal custody. Sentencing is scheduled for February 2, 2027, before U.S. District Judge Noel Wise. He faces a maximum penalty of 10 years in prison and a $250,000 fine.

Was this report accurate and useful?

Sources

Revision history

  1. Version 19 Sept 2026, 08:52current

    First published.

How we work

This site models an investigative reporter rather than a wire desk. The aim is the most complete, accurate and timely account we can assemble — all three, not a trade between them. Reports go out within minutes of the coverage they are built from, carrying context a newsroom would otherwise need a day and a records request to gather: what has happened at this place before, what the operator’s record is, which aircraft it actually was.

Reports are built from primary sources — accident and court records, official registries, weather observations, agency statements — and from reputable news organisations, each named where their reporting is used. Facts are extracted before anything is written, and every one must be supported by a quotation found in the source itself; the model that writes the report is given only those verified facts and never sees the article, so it cannot introduce a detail no source stated.

Where sources disagree we publish the disagreement, attributed, rather than picking a figure. Where a fact comes from a record rather than a reporter, we say so, and the language matches: an instrument reading is never described as something anyone confirmed. Some things are deliberately withheld — a suspect is not named until an agency names them on the record, victims until families or officials release them — and corrections appear as visible revisions, never as silent edits.

None of that makes a report true. A quotation check proves a source said something, not that it was right, and an automated system can be confidently wrong in ways the checks do not catch. If something here is wrong, the feedback above is how it gets found. The full methodology, including what we refuse to publish.