A romance scammer didn’t just take a victim’s life savings. The victim then received a tax bill, a detail that should stop anyone working on anti-fraud policy cold.

That is the core issue raised as Congress considers how to respond to AI-enabled fraud: it should also revisit a tax code that treats victims differently depending on whether they were manipulated with promises of wealth or promises of love. In other words, the harm is real and measurable, but the tax outcome can turn on the type of bait the scam uses. That creates a perverse incentive and an unequal burden that has nothing to do with the victim’s ability to pay and everything to do with the scammer’s script.

This matters now because the policy conversation around fraud is accelerating. AI-enabled scams are not theoretical. They are getting more scalable, more convincing, and more tailored, which means more people are likely to be targeted through new channels and more traditional ones. Congress is trying to catch up with tools that can synthesize text, impersonate voices, and automate personalization. But even if lawmakers build stronger enforcement for scammers, victims can still get squeezed after the fact. A tax bill after a romance scam is not a minor administrative inconvenience. It is a follow-on penalty that can push already devastated households further into financial distress.

The tax code angle is especially important because it highlights something regulators often miss when chasing the next fraud wave: the incentive structure behind the scenes. If victims experience different tax treatment depending on whether the manipulation is framed as wealth or love, then the tax system effectively acts like a second battlefield. That means scammers can potentially optimize their approach, even unintentionally, by nudging victims into outcomes that are better for the scammer and worse for the victim. Even if current law does not explicitly “encourage” romance scams, the unequal treatment creates uneven recovery prospects, which changes the practical stakes of how fraud plays out.

At a high level, fraud response usually focuses on three levers: prevention, detection, and accountability. Congress can strengthen detection and enforcement, and it can shape how platforms handle AI-generated impersonation. But the “accountability” part includes consequences for scammers, and the “prevention” part includes helping people avoid falling for manipulation. What falls through the cracks is the victim’s post-loss reality: how quickly can they restore stability, what obligations do they face, and how much of their remaining resources are eroded by systems that were not designed for fraud aftermath.

This is where boardrooms and C-suites should pay attention, even if they are not lawmakers. Companies that touch payments, identity, financial services, or fraud prevention are already living in a world where the public expects both swift intervention and real accountability. If the tax code compounds losses, trust in the broader system takes another hit. That can affect everything from customer relationships to regulatory scrutiny of financial institutions and platforms. When a headline involves life savings and a tax bill, it signals a gap between what enforcement tries to stop and what financial households still experience after the scam ends.

There is also a second-order policy risk for executives: siloed thinking. Fraud bills often divide responsibilities across agencies and committees, while tax policy lives in its own lanes. Yet victims experience the overlap directly. Congress considering AI-enabled fraud should therefore connect the dots between fraud enforcement and tax treatment. If policymakers address only the scammers but not the tax consequences for victims, then the headline problem does not fully resolve. It just changes costumes.

For peers in similar roles, the strategic stakes are straightforward. Fraud operations are becoming more sophisticated. AI can lower the cost of harassment, impersonation, and persuasion. But the “real cost” to society is not just the money taken by the scam. It is the downstream financial damage that follows, including tax outcomes that differ by scam narrative. A credible response requires a full view of the lifecycle of fraud, from the initial manipulation to the aftermath that determines whether victims can recover.