Papers Fraud Detection
“Fraud Detection” 태그가 달린 논문 729편 · 필터 해제
TeleAntiFraud 2.0: A Refreshable, Profile-Grounded, and Audio-Based Benchmark for Telecom Fraud Detection
Telecom fraud scripts evolve rapidly and are often designed to resemble routine service conversations, creating two key requirements for audio-based telecom-fraud evaluation. First, benchmarks must incorporate newly obse…
Fraud DetectionFRAUDSkill: Structured Frozen-Weight Skill Optimization for Audio Anti-Fraud Detection
Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their deployment, however, requires predictions to follow a predefined labe…
Fraud DetectionEvidence-Consistent Generative Detection under Scenario-Level Distribution Shift
Conventional in-distribution evaluation can overestimate robustness when training and test data share recurring task-specific patterns or surface cues. This risk is especially relevant in social-engineering fraud detecti…
Fraud DetectionTraceable LLM Reasoning for Fake-Order Fraud Detection
Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provid…
Reinforcement LearningDomain AdaptationFraud DetectionMeasuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models
Synthetic tabular data are valued for preserving not just column-wise marginals but inter-column dependency. Yet the most commonly reported certification score, a linear (logistic-regression) classifier two-sample test (…
Fraud DetectionNeutralizing Structural Inequality in the Nigerian FinTech Sector
Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities. This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detectio…
Fraud DetectionTSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics…
Node ClassificationLink PredictionFraud DetectionPiercing Gilbreath's Conjecture: From Deep Number Theory Insights to Fintech and Cybersecurity
I propose a new methodology to attack the fascinating Gilbreath's conjecture about prime numbers, first posted in 1878 and unsolved to this day. The problem statement is rudimentary: kids can understand it. However, desp…
Fraud DetectionInterpretable vs Learned Encoders for High-Cardinality Fraud Detection
A total of seven categorical encoding methods were tested on the IEEE-CIS fraud benchmark dataset (590,540 records, 3.5% positives, 8 high-cardinality columns). The encoders were evaluated using a stratified 5-fold cross…
Fraud DetectionNode-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Existing approaches generally fall into two paradigms. GNN-based metho…
Graph Anomaly DetectionFraud DetectionDialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection
Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders. Early detection at FNOL remains a persistent challenge. Existing a…
Fraud DetectionDG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions
Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text,…
Fraud DetectionBeyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection
Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks. Real-world fraud graphs pose two coupled …
Representation LearningGraph Neural NetworkContrastive LearningFraud DetectionEMA-FS: Accelerating GBDT Training via Gain-Informed Feature Screening
Gradient Boosted Decision Trees (GBDT), exemplified by LightGBM, spend a dominant fraction of training time -- typically 65-70% -- constructing per-feature histograms. Existing approaches such as random feature subsampli…
Fraud DetectionOpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial relevance of benchmark tasks is often overlo…
Fraud DetectionA Fair Evaluation of Graph Foundation Models for Node Property Prediction
Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention. While many different types of models …
Node Property PredictionRecommendation SystemsGraph Neural NetworkFraud DetectionMulti-Stream Temporal Fusion for Financial Fraud Detection
Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulen…
Fraud DetectionFeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism
Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (GraphRAG). As graphs scale to billions of edg…
Fraud DetectionGraph EmbeddingSOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training
Many machine learning applications rely on heterogeneous event streams to make predictions, either causally as events arrive or bidirectionally over complete sequences. We propose SOHET (Sequence Of Heterogeneous Events …
Fraud DetectionTMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network
In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues,…
Graph Neural NetworkContrastive LearningFraud Detection