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Yes. A year-long investigation released October 13, 2026, by the Democratic minority of the U.S. Senate Joint Economic Committee found that most artificial-intelligence models Tinder used to detect scammers and other bad actors had not been updated for years as of the second half of 2025. The committee also said the median Tinder scammer’s profile views rose by more than 700% worldwide between May 2024 and December 2025.
Match Group disputes neither the remediation it described nor its cooperation with investigators, but its reported improvements have not been independently evaluated in the committee’s release. The findings concern Tinder’s fraud controls most directly, with the inquiry also covering Hinge, Match and Plenty of Fish.
What the Senate investigation found
The committee reviewed more than 670 pages of internal documents supplied by Match Group. Its release says Match knew Tinder was more likely to show a profile when it belonged to a scammer or another bad actor than when it belonged to a regular user. The committee presented that as part of a broader pattern of safety shortcomings, not as an isolated software error.
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| Finding | Detail reported by the committee |
|---|---|
| Model maintenance | Most Tinder AI models used to detect scammers and other bad actors had not been updated for years as of the second half of 2025. |
| Exposure to users | The number of profile views received by a median Tinder scammer increased by more than 700% globally from May 2024 to December 2025. |
| Ranking behavior | Match knew Tinder was more likely to show profiles belonging to scammers or other bad actors than profiles belonging to regular users. |
| Investigation materials | Match engaged with investigators and supplied more than 670 pages of internal documents. |
The release came from the committee’s Democratic minority, so it is a congressional investigative finding rather than a court judgment. It describes evidence and company documents gathered by investigators; it does not establish that every Tinder user was exposed to a scammer or that every account-ranking decision had the same effect.
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Why stale scam filters are a serious problem
Fraud-detection systems learn from patterns such as message language, payment requests, device behavior, account links and reports from users. Scammers can change those patterns quickly. A model trained on last year’s scripts may miss a new investment pitch, a new image set or a new way of moving victims to encrypted messaging.
An expert quoted by committee staff said Tinder would need to retrain its machine-learning models at least monthly, depending on its monitoring systems and other performance measures. The expert said retraining only once or twice a year would indicate that fraud detection was not being taken seriously. That is an expert standard cited in the release, not a statutory requirement imposed on every dating service.
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What Match Group says it changed
Face Check rollout
Match announced the U.S. rollout of Face Check on Tinder in October 2025 and deployed it nationwide in early December. Match told the committee that, within one month of nationwide deployment, interactions with bad actors—including spam, bots and scammers—fell by more than 70% in the United States. The company said the reduction lasted at least seven months.
Those figures are company-reported results presented in the committee’s release. The material does not provide an independent audit, a public methodology, a comparison group or a platform-wide result covering every country and service.
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Match also told investigators that it introduced a new AI model during the first half of 2026 to examine profiles and other information for scam indicators. The release does not establish how often that model is retrained, how many accounts it reviews, its false-positive rate or how quickly users can appeal an incorrect removal.
How large are romance-scam losses?
Official sources cited by the committee put U.S. losses in different ranges because they refer to different reporting periods and descriptions. They should not be combined into one precise annual total.
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| Source and period | Reported amount | Qualification |
|---|---|---|
| Federal Trade Commission data for the 2025 reporting year, as described in the committee release | Nearly $1.5 billion | The committee described this as almost a 650% increase since 2019. |
| Bipartisan Senate letter dated September 24, 2025 | At least $1.3 billion annually | This is the FTC figure cited in that letter, with its own reporting context. |
Romance scams often begin with a relationship or investment story and end with requests for money, cryptocurrency, gift cards, account access or personal documents. Reported losses understate the real harm because many victims do not report or recover the money.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesWhat users should do if a match looks fraudulent
- Stop sending money or documents. Do not pay a supposed emergency expense, “release” an investment, buy gift cards or share one-time banking codes.
- Preserve evidence. Save the profile URL or username, messages, phone numbers, payment instructions, receipts and dates before blocking the account.
- Report the account inside the app. Use the profile or conversation reporting control and identify the behavior as suspected fraud, impersonation or financial solicitation.
- Contact the payment provider immediately. Ask a bank, card issuer, money-transfer service or cryptocurrency exchange whether a transaction can be stopped or reversed.
- Report the crime to authorities. File a report with the Federal Trade Commission’s fraud-reporting service, the FBI’s Internet Crime Complaint Center and local law enforcement where appropriate.
- Secure exposed accounts. Change reused passwords, enable multifactor authentication and warn contacts if the scammer obtained access to an email or social account.
Will dating apps have to warn you after a scammer is banned?
The Congressional Record describes a proposed fraud-ban notification requirement for online dating services. Under the measure described there, a user would be notified if they had interacted with an account later banned for fraud. A notification could be delayed at law-enforcement request, and implementation would begin one year after enactment.
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The record excerpt does not establish that the requirement was enacted, nor does it establish an effective date. Users should therefore not assume that every service currently provides this warning. The proposal also raises practical questions about what counts as an interaction, how services handle false bans and how much information a warning would disclose.
Related FTC actions are separate matters
Billing and advertising case
In August 2025, the FTC announced that Match Group agreed to pay $14 million and change practices involving deceptive guarantees, account lockouts and cancellation procedures. That matter concerns billing and advertising conduct; it is not proof of the committee’s findings about scam-detection models.
Privacy case
In March 2026, an FTC case involving OkCupid and Match Group Americas alleged that personal information, including photos and location data, was shared with an unrelated third party contrary to privacy promises. The case page records a stipulated order. Privacy disclosures are a separate issue from detecting romance scams.
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Questions that should shape dating-app safety reporting
The investigation does not provide a complete, apples-to-apples safety ranking of dating platforms. To evaluate any service, users and regulators should ask:
- Does the service use identity or liveness verification, and when is it required?
- How frequently are scam-detection models retrained, and how quickly are new attack patterns added?
- How long does it take to remove a reported account?
- Will users be notified after messaging an account later banned for fraud?
- How does the service measure false positives, appeals and mistaken removals?
- Are claims about reduced scam exposure measured independently or reported only by the company?
Those measures matter because a large reduction in bad-actor interactions is meaningful only when the service explains what was counted, over what period, and whether independent reviewers can reproduce the result.
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