{"total":48,"rows":[{"id":"2411.05815","title":"Graph Neural Networks for Financial Fraud Detection: A Review","created":"2024-11-01","cited_by_count":25,"area":null},{"id":"2405.19383","title":"Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation","created":"2024-05-29","cited_by_count":7,"area":null},{"id":"2407.10916","title":"When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark","created":"2024-07-15","cited_by_count":6,"area":null},{"id":"2401.00965","title":"Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective","created":"2024-01-01","cited_by_count":5,"area":null},{"id":"2402.08593","title":"Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2404.00060","title":"Temporal Graph Networks for Graph Anomaly Detection in Financial Networks","created":"2024-03-27","cited_by_count":4,"area":null},{"id":"2507.14785","title":"Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs","created":"2025-07-20","cited_by_count":3,"area":null},{"id":"2405.08017","title":"Translating Expert Intuition into Quantifiable Features: Encode Investigator Domain Knowledge via LLM for Enhanced Predictive Analytics","created":"2024-05-11","cited_by_count":2,"area":null},{"id":"2503.16901","title":"TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs","created":"2025-03-21","cited_by_count":2,"area":null},{"id":"2402.10392","title":"Pretext Training Algorithms for Event Sequence Data","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2411.02926","title":"Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering","created":"2024-11-05","cited_by_count":1,"area":null},{"id":"2412.00241","title":"MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes","created":"2024-11-29","cited_by_count":1,"area":null},{"id":"2503.10058","title":"Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions","created":"2025-03-13","cited_by_count":1,"area":null},{"id":"2503.24259","title":"Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review","created":"2025-03-31","cited_by_count":1,"area":null},{"id":"2509.12730","title":"A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2601.03149","title":"PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback","created":"2026-01-06","cited_by_count":1,"area":null},{"id":"2604.01315","title":"Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling","created":"2026-04-01","cited_by_count":1,"area":null},{"id":"2404.14746","title":"A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation","created":"2024-04-23","cited_by_count":0,"area":null},{"id":"2411.02174","title":"Behavioral Sequence Modeling with Ensemble Learning","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2412.10895","title":"Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2506.00654","title":"Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection","created":"2025-05-31","cited_by_count":0,"area":null},{"id":"2506.04292","title":"GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2507.19402","title":"FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report","created":"2025-07-25","cited_by_count":0,"area":null},{"id":"2509.11595","title":"AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2509.18499","title":"Hybrid Data can Enhance the Utility of Synthetic Data for Training Anti-Money Laundering Models","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2601.21446","title":"Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.19207","title":"HybridFL: A Federated Learning Approach for Financial Crime Detection","created":"2026-02-22","cited_by_count":0,"area":null},{"id":"2603.01863","title":"Tide: A Customisable Dataset Generator for Anti-Money Laundering Research","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.19314","title":"DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.02899","title":"Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2604.17420","title":"TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2604.21093","title":"TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.06433","title":"Federated Layer-Wise Exchange for Cross-Client Money Laundering Pattern Detection","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.14744","title":"Mechanical Enforcement for LLM Governance:Evidence of Governance-Task Decoupling in Financial Decision Systems","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.26243","title":"Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.04672","title":"Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.26664","title":"TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2607.05103","title":"Choosing a parallel heterogeneous ensemble method for tabular classification","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.15447","title":"Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework","created":"2026-08-15","cited_by_count":0,"area":null},{"id":"2608.15768","title":"Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2608.22389","title":"KONTOGRAPH: Verified Point-in-Time Feature Consistency and Amortised Explanation for Real-Time Anti-Money Laundering under a 200 ms Decision Budget","created":"2026-08-23","cited_by_count":0,"area":null},{"id":"2609.03036","title":"Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.11993","title":"FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.30684","title":"PixSim: a calibrated open-source simulator of instant-payment fraud, recovery and interdiction under analyst capacity constraints","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2610.02122","title":"Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.13627","title":"AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.18054","title":"Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption","created":"2026-10-13","cited_by_count":0,"area":null},{"id":"2610.18164","title":"Random Probing for Structural Self-Interactions in Graph Neural Networks","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}