Within days of being released from his seventh prison term for romance fraud, Raymond McDonald was back on a dating app looking for his next victim. Over more than 20 years he had racked up 58 convictions, mostly for fraud and theft, while telling lies on an industrial scale and taking thousands of pounds from women for holidays and weddings which were never going to happen. This time when he went looking, the BBC was waiting. He thought he was having a date with Kaye, but instead found himself being approached by a BBC reporter and camera crew. He had met Kaye online and, calling himself Rob, told her he was a deep-sea diver looking for a wife.

A mother of two says she was left penniless after giving her savings to Tinder predator Christopher Harkins in a fake investment scam. The pair matched on the dating app in London in 2020. Caitlyn - not her real name - told how the fraudster and rapist initially tried to talk her into going on holiday with him - a regular ruse of Harkins, now 38. When she said she couldn't afford a holiday, he offered to help by doubling what money she had via his foreign currency exchange business. She's one of four women the BBC is aware of who were targeted by Harkins in the capital - where he fled to after his crimes were exposed in Scotland.

DGraph overcomes many limitations of current GAD datasets.

We fix this split and provide it in the public dataset.

Things to Do in L.A. A shadowy L.A. crime ring is hijacking the IDs of foreign scholars, fraud expert says This is read by an automated voice. Please report any issues or inconsistencies here . An identity theft ring believed to be based in the Burbank area is stealing Social Security Numbers of former foreign scholars. Private fraud investigators suspect the operation is connected to Armenian organized crime groups known for sophisticated financial crimes. Using apartments in the San Fernando Valley and Glendale area, a shadowy group of identity thieves has been quietly exploiting a new kind of victim -- foreign scholars who left the U.S. years ago but whose Social Security numbers still linger in American databases, according to a cybercrime expert.

Generative AI is supercharging insurance fraud by making it easier to falsify accident evidence at scale and in rapid time. Insurance fraud is a pervasive and costly problem, amounting to tens of billions of dollars in losses each year. In the vehicle insurance sector, fraud schemes have traditionally involved staged accidents, exaggerated damage, or forged documents. The rise of generative AI, including deepfake image and video generation, has introduced new methods for committing fraud at scale. Fraudsters can now fabricate highly realistic crash photos, damage evidence, and even fake identities or documents with minimal effort, exploiting AI tools to bolster false insurance claims. Insurers have begun deploying countermeasures such as AI-based deepfake detection software and enhanced verification processes to detect and mitigate these AI-driven scams. However, current mitigation strategies face significant limitations. Detection tools can suffer from false positives and negatives, and sophisticated fraudsters continuously adapt their tactics to evade automated checks. This cat-and-mouse arms race between generative AI and detection technology, combined with resource and cost barriers for insurers, means that combating AI-enabled insurance fraud remains an ongoing challenge. In this white paper, we present UVeye layered solution for vehicle fraud, representing a major leap forward in the ability to detect, mitigate and deter this new wave of fraud.

Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, existing graph fraud detection methods typically use preprocessed node embeddings and predefined graph structures to reveal fraudsters, which ignore the rich semantic cues contained in raw textual information. Although Large Language Models (LLMs) exhibit powerful capabilities in processing textual information, it remains a significant challenge to perform multimodal fusion of processed textual embeddings with graph structures. In this paper, we propose a \textbf{M}ulti-level \textbf{L}LM \textbf{E}nhanced Graph Fraud \textbf{D}etection framework called MLED. In MLED, we utilize LLMs to extract external knowledge from textual information to enhance graph fraud detection methods. To integrate LLMs with graph structure information and enhance the ability to distinguish fraudsters, we design a multi-level LLM enhanced framework including type-level enhancer and relation-level enhancer. One is to enhance the difference between the fraudsters and the benign entities, the other is to enhance the importance of the fraudsters in different relations. The experiments on four real-world datasets show that MLED achieves state-of-the-art performance in graph fraud detection as a generalized framework that can be applied to existing methods.

Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads to imbalanced transmission of global topological information and increased risk of node-specific information being overwhelmed during aggregation due to the imbalance between fraud and benign nodes. In this paper, we first summarize the impact of topology and class imbalance on downstream tasks in GNN-based fraud detection, as the problem of imbalanced supervisory messages is caused by fraudsters' topological behavior obfuscation and identity feature concealment. Based on statistical validation, we propose a novel dual-view graph representation learning method to mitigate Message imbalance in Fraud Detection (MimbFD). Specifically, we design a topological message reachability module for high-quality node representation learning to penetrate fraudsters' camouflage and alleviate insufficient propagation. Then, we introduce a local confounding debiasing module to adjust node representations, enhancing the stable association between node representations and labels to balance the influence of different classes. Finally, we conducted experiments on three public fraud datasets, and the results demonstrate that MimbFD exhibits outstanding performance in fraud detection.