Fraud Detection
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Related Articles from SNS
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
arXiv:2606.08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers. Existing methods fall at least one of these requirements: automated machine learning systems search a fixed numerical space without semantic awareness of the dataset; graph neural network-based methods require pre-defined relational graphs and...
The Fundamental Limits of Fraud Detection in Card Payment Networks
Announce Type: replace Abstract: Card payment fraud detection is usually framed as a supervised classification problem. Although this approach has generated practical progress, improvement has remained incremental despite major advances in model architecture. We argue that this is not mainly a failure of function approximation or optimization, but a consequence of structural information impairments inherent to the payment ecosystem.
Bernoulli CUSUM and Bayes-Optimal Detection Ceilings for Trust Fraud in Sparse Rating Networks
arXiv:2606.05090v1 Announce Type: new Abstract: Sequential trust detection in rating networks relies on continuous observation models that fail on real data. On Bitcoin-OTC, 56\% of ratings take a single value under standard mapping, breaking the distributional assumptions that parametric detectors require. This paper makes three contributions.
Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks
Announce Type: new Abstract: 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.
Robust Ensemble of Selectively Strengthened and Augmented Predictors
Announce Type: new Abstract: Evasion attacks present a significant challenge to the robustness of machine learning (ML)-based classifiers, particularly in critical applications such as fraud detection and cybersecurity. Although existing defense mechanisms are effective in some settings, they often suffer from limited generalizability and do not systematically improve model robustness across diverse attack scenarios. To address these limitations, we introduce Robust Ensemble of Selectively...
AuditFraudBench: Benchmarking Audit Judgment in Detecting Fraudulent Misstatements
Announce Type: new Abstract: Large language models (LLMs) have shown strong performance in financial analysis and surface-level factual error detection, yet their ability to identify fraudulent financial misinformation in audited corporate reporting remains underexplored. Existing financial and audit benchmarks mainly focus on factual verification, numerical reasoning, rule compliance, or audit workflows, but rarely evaluate misleading disclosure narratives or management explanations that...
The fake police scam stealing millions from Australia's Chinese community
Fake police scam targets Chinese diaspora with threats and forged documents Sat 6 Jun 2026 at 5:30am It's a warm evening in a small Australian town. A man dressed in a shirt and tie prepares to record a statement he believes will be used in court.
HMRC issues warning to TikTok users as two arrested over suspected £153m tax scam
HMRC issues warning to TikTok users as two arrested over suspected £153m tax scam The scheme allegedly involved individuals posting ads on the popular social media platform - Bookmark HM Revenue and Customs (HMRC) has issued a stark warning to TikTok users following the uncovering of a suspected £153 million tax fraud scam. The public is being cautioned against sharing their tax details online after the discovery. The scheme allegedly involved individuals posting advertisements on the...
Sex criminals, gang members abused child immigration program to enter US, DHS reveals
EXCLUSIVE — The Department of Homeland Security (DHS) says that thousands of illegal aliens — many of them sex criminals, murderers and known members of brutal gangs — have abused a program meant to protect at-risk minors in order to gain entry into the United States. A report from U.S. Citizenship and Immigration Services (USCIS) first obtained by Fox News Digital reveals the extent to which the Special Immigrant Juvenile (SIJ) program is riddled with fraud, and oftentimes exploited by the...