The paper introduces Centroid‑Guided Contrastive Loss (CGCL), a novel loss function that combines classification with dense clustering to improve fraudulent job posting detection. CGCL reshapes the latent space using a centroid‑driven top‑k push‑and‑pull mechanism, ensuring accurate decision boundaries while preserving compact clustering of similar fake posts. Experiments on the EMSCAD benchmark show that CGCL achieves state‑of‑the‑art performance.
arXiv:2507. 14706v2 Announce Type: replace-cross Abstract: Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the often subtle patterns that separate fraud from legitimate activity.
The paper presents a signal-theoretic machine learning framework to detect deceptive online job ads that facilitate forced labour. Using 464 verified cases from nine countries and 21 industries, the authors build multimodal models that combine computer vision, natural language processing, and semantic embeddings, achieving ROC‑AUC scores between 0.87 and 0.97. SHAP analysis identifies text quality, risk language, and visual features as key discriminators, and the authors deliver a proof‑of‑concept decision support system that outputs interpretable risk scores for practitioners.
arXiv:2607. 04184v1 Announce Type: new Abstract: Market manipulation is the dubious practice of manipulating stock prices in order to make a quick profit, which truly degrades confidence on trading platforms.
arXiv:2609.14234v1 Announce Type: cross Abstract: Financial fraud in corporate transaction networks has grown more coordinated and harder to detect with rule-based engines and with classical learning...
IDSPACE is a new synthetic document generator designed to aid the evaluation of digital identity verification systems. It uses model‑guided Bayesian optimization to tune generation parameters for visual similarity and prediction consistency, separates user‑defined metadata from low‑level control settings, and supports scanned and mobile‑captured documents. Experiments demonstrate that IDSPACE improves evaluation consistency by 15‑45 % over baselines, boosts training accuracy by up to 9 %, and increases SSIM similarity by 10 %, while providing a dataset of 359,240 synthetic documents across ten European ID types.
arXiv:2607. 11107v1 Announce Type: new Abstract: Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems.
Evi-VN is a new framework for graph neural network (GNN) fraud detection that injects evidence from diverse modalities—structured records, text, images, and audio—into virtual node representations. It targets shared hard regions where standard GNNs fail to distinguish well‑disguised fraudsters from legitimate users, correcting blind spots without disrupting reliable predictions. Experiments on bot, fake‑review, refund‑evidence, and telecom‑fraud datasets demonstrate its effectiveness across generic, fraud‑specific, and unseen GNNs.
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection.
arXiv:2606. 28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks.
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.
arXiv:2609.08445v1 Announce Type: new Abstract: Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by superv...