God told them to sell crypto. A pastor and his wife created a cryptocurrency and hawked it in Christian communities. When it came crashing down, investors lost millions and they were accused of fraud. When Eli Regalado first heard God speak to him, he wondered whether he was hallucinating. Now he likens the experience to having "a thought that is not my thought." Divine words echo in his mind like a line from a movie or the memory of a loved one's voice. "It's not'You better do this,'" he says. Holy messages arrive daily while Eli is praying, reading, or watching television. Sometimes they surface in prophetic dreams or missives from strangers. Occasionally, they appear midsentence, when he pauses to ask, "Lord, what do you want to say here?" Eli's wife, Kaitlyn, tends to get heavenly dispatches in the shower, when she finally has a moment to herself. Other times, she seeks counsel from above. "I'll be writing in my journal and praying and asking questions and just believing what I'm hearing is Him," she says. God's directives have been manifold. According to the Regalados, He told them to get married, buy a house, and start having kids. When Eli owned a marketing firm in Colorado, He told him what to name it, whom to hire, and which clients to take on. Then God told him to start preaching in his living room and online. In 2021, when Eli was 41 and Kaitlyn was 28, divine guidance steered them in an unexpected new direction: crypto.

The Consumer Affairs Agency has established a group to use generative artificial intelligence technology to more quickly detect investment scams and other fraudulent schemes. The Consumer Affairs Agency said Tuesday it will step up its fight against investment and other fraud promising lucrative returns by using generative artificial intelligence technology to enhance information analysis. A group established within the agency the same day will utilize the technology to find schemes with similar patterns to past fraud cases and malicious business operators to enable quicker intervention, the agency said, announcing a set of measures against the type of scheme that attracts large amounts of money by promising high returns before eventually collapsing. "Recovery is difficult once the damage (from fraud) is done, so it is important to respond quickly," consumer affairs minister Hitoshi Kikawada told a news conference. The new group will run a generative AI system to analyze cases reported to the National Consumer Affairs Center's information system for consultation, which number about 900,000 annually.

The Trump administration launched a fraud tracking dashboard documenting $230 billion in suspected fraud uncovered by the White House task force chaired by JD Vance.

Fraud expert David Maimon warned Congress that criminals are using artificial intelligence to create deepfakes and fake documents to bypass government identity verification systems.

Cases of fraud in the UK have surged with criminals using AI to manipulate people and even marrying victims of romance scams to steal more money. More than four million cases in which money was lost were reported last year - the equivalent of nearly eight on average every minute, according to new figures. The total has increased by more than one million in two years, with almost £1.3bn The enormous scale of the problem could only be tackled if tech companies stepped up monitoring and security of their platforms, the banking trade body said. Banks said fraud posed a national security threat given the impact on victims and the huge sums stolen by organised criminals.

Fraud detection in payment networks relies on labels generated through heterogeneous and imperfect observation processes, yet existing approaches treat fraud as a homogeneous binary variable. We show that this assumption is structurally incorrect and leads to provable inefficiency. We introduce an observation-mechanism taxonomy that partitions fraud into five classes, each defined by a distinct censorship and labeling pipeline. We prove that estimating fraud rates separately by class and aggregating strictly dominates pooled estimation, with the efficiency gap characterized as a Jensen penalty arising from heterogeneous observation rates. For each class, we derive the binding theoretical constraint on detection, including endogenous label corruption, structural non-observability, and feature non-informativeness. These results establish that fraud detection is fundamentally a collection of distinct estimation problems, each governed by its own observation structure and detection limit.

Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates: authorization (declined transactions generate no labels), issuer reporting (unreported fraud is invisible), and delay (pending chargebacks are missing at training time). Labels that do arrive may be corrupted by first-party misuse or issuer misclassification. A companion paper [arXiv:2605.27557] proved that these four impairments impose a minimax lower bound on detection performance. This paper asks: can that bound be achieved? We formalize the observation pipeline as a sequential missing-data problem with three propensity stages and a corruption layer, and construct the Sequential Triply Robust (STR) estimator. The STR corrects for all four impairments simultaneously and achieves the semiparametric efficiency bound -- no estimator can have lower asymptotic variance. It is sequentially triply robust: at each gate, consistency requires only that either the propensity model or the outcome regression is correctly specified, not both. We provide corruption correction via noise-rate-adjusted pseudo-labels, empirical Bayes shrinkage to stabilize inverse-propensity weights for small issuers, a plug-in variance estimator yielding valid confidence intervals, and a Bernstein concentration inequality for finite-sample guarantees. On the operational side, we derive the optimal training delay -- the maturity window that minimizes the sum of label-quality loss and model staleness -- and prove that the STR permits training on data that is days old rather than months old, decoupling model freshness from the chargeback maturity cycle. The STR provably dominates naive chargeback-based training in mean squared error for any sample size.

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VP JD Vance and FTC Chairman Andrew Ferguson lead Trump's Anti-Fraud Taskforce, citing $250 billion in annual losses and a new strategy to stop fraud before payouts.

OpenAI made the perfect tool for scammers. Donald Trump is on TikTok doing his morning routine. "Get ready with me for a big day," reads the caption, as the president holds a makeup brush to his cheek. The scene is a still, ostensibly a screenshot of a TikTok clip. Like so much other AI-generated slop coursing through the internet, the image is fake and ridiculous.