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.

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Federal data shows identity theft losses can extend far beyond direct financial damage as victims spend weeks repairing credit files.

Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Criminals are increasingly exploiting AI technology to take over people's mobile, banking and online shopping accounts, the UK's leading anti-fraud body has warned. Last year, a record number of scams were reported to the national fraud database, fuelled by AI, which allows for large-scale deception on "industrialised" levels, according to Cifas, the fraud prevention organisation. Its report showed 444,000 cases of fraud were reported by its members last year - a 6% increase on 2024.

Identity theft involving stolen driver's licenses creates lasting legal exposure unlike credit card fraud, as license numbers cannot be changed and require extensive cleanup efforts.

Minnesota Is Just the Beginning. California and New York Are'Next' The Trump administration appears to be planning to leverage the same playbook used in Minnesota to go after other blue states. The Trump administration appears to be deploying the same playbook it used in Minnesota --leveraging allegations of fraud to justify significant federal oversight --in other blue states across the country, starting with California and New York. "POTUS loves Minnesota and the people. It's a state where he received historic Republican support, and he has long called out [Governor Tim] Walz for his incompetence and terrible leadership," a senior White House official tells WIRED.

Things to Do in L.A. Tim Walz isn't the only governor plagued by fraud. Minnesota Gov. Tim Walz said he would not seek a third term amid attacks over a fraud scandal involving child care funding. This is read by an automated voice. Please report any issues or inconsistencies here . California has lost billions to cheats in the last few years, leaving Newsom vulnerable to the same sort of attack that took down Walz.