News & Reviews News Wire Former Amtrak employee and husband plead guilty in COVID unemployment insurance fraud case

Former Amtrak employee and husband plead guilty in COVID unemployment insurance fraud case

By Trains Staff | November 23, 2022

| Last updated on February 11, 2024

Claims reportedly yielded almost $1 million in payments

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U.S. Department of Justice sealLOS ANGELES — A former Amtrak employee and her husband have pleaded guilty to federal criminal charges for conspiring to steal nearly $1 million in COVID-19-related unemployment insurance benefits, while she also fraudulently obtained more than $63,000 in sickness benefits while working at Amtrak.

The U.S. Attorney’s Office for the Central District of California announced in a press release that Lizette Berrios Lathon, 45, of Moreno Valley, Calif., pleaded guilty to charges of conspiracy to commit mail and wire fraud, aggravated identity theft, and wire fraud. Her husband, Kenneth Andrew Lathon, 48, of Moreno Valley, pleaded guilty to conspiracy to commit mail and wire fraud, aggravated identity theft, and unlawful possession of a firearm by a convicted felon

From 2014 to the present, Lizette Lathon operated tax preparation businesses in addition to working for Amtrak. Using information obtained from former tax clients without their permission, she submitted at least 44 false claims for unemployment insurance benefits, collecting more than $998,000. At the same time, she claimed false sickness benefit payments from the Railroad Retirement Board between 2014 and 2020.

The Lathons will face sentencing at an April 13, 2023 hearing before U.S. District Judge Fernando M. Olguin. Lizette Lathon will face a maximum sentence of 42 years in prison; Kennty Lathon will face a maximum sentence of 32 years.

 

6 thoughts on “Former Amtrak employee and husband plead guilty in COVID unemployment insurance fraud case

  1. The couple will be elderly should they complete their respective maximum sentences if they live that long. Digital technology is complex but traceable.

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