AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions

• URL: http://arxiv.org/abs/2509.11595v1
• Date: Mon, 15 Sep 2025 05:25:46 GMT
• Title: AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions
• Abstract summary: AMLNet is a knowledge-based multi-agent framework with two coordinated units: a regulation-aware transaction generator and an ensemble detection pipeline.<n>The generator produces 1,090,173 synthetic transactions spanning core laundering phases and advanced typologies.<n>The detection ensemble F1 0.90 adapts to the external SynthAML dataset, indicating architectural generalizability.
• Score: 3.1459290341742676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anti-money laundering (AML) research is constrained by the lack of publicly shareable, regulation-aligned transaction datasets. We present AMLNet, a knowledge-based multi-agent framework with two coordinated units: a regulation-aware transaction generator and an ensemble detection pipeline. The generator produces 1,090,173 synthetic transactions (approximately 0.16\% laundering-positive) spanning core laundering phases (placement, layering, integration) and advanced typologies (e.g., structuring, adaptive threshold behavior). Regulatory alignment reaches 75\% based on AUSTRAC rule coverage (Section 4.2), while a composite technical fidelity score of 0.75 summarizes temporal, structural, and behavioral realism components (Section 4.4). The detection ensemble achieves F1 0.90 (precision 0.84, recall 0.97) on the internal test partitions of AMLNet and adapts to the external SynthAML dataset, indicating architectural generalizability across different synthetic generation paradigms. We provide multi-dimensional evaluation (regulatory, temporal, network, behavioral) and release the dataset (Version 1.0, https://doi.org/10.5281/zenodo.16736515), to advance reproducible and regulation-conscious AML experimentation.

Related papers

• SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center [0.0]
  Sentinel-RL is an agentic-SOC architecture that decouples topological reasoning from semantic reasoning.<n>We instantiate the system on the LANL Comprehensive, Multi-Source Cyber-Security Events dataset and the Indiana University Quartz HPC cluster.
  arXiv  Detail & Related papers  (2026-09-03T17:49:12Z)
• Long-Range Indirect Control-Flow Prediction in Stripped Binaries via Dual Virtual Hubs and Multi-Task Graph Learning [44.79384874347874]
  We present ICFlowNet, a unified framework for long-range ICF prediction in stripped binaries.<n>ICFlowNet introduces candidate-aware Dual Virtual Hubs, a Global Code Hub and a Global Data Hub.<n>We also develop a leakage-aware, noise-controlled pipeline with package-level splits, function-level mnemonic-hash deduplication, and a clean test protocol.
  arXiv  Detail & Related papers  (2026-09-03T02:17:23Z)
• Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework [0.0]
  This paper develops and evaluates a transaction-monitoring framework aligned to the Rwandan AML/CFT regime.<n>Using a synthetic dataset of 9,504,852 transactions with 17 typologies, we engineer account-centric behavioural features.<n>We map score bands to Rwanda-relevant analyst and STR/SAR escalation, and outline a staged path from synthetic prototyping to real-data validation with the National Bank of Rwanda.
  arXiv  Detail & Related papers  (2026-08-15T23:19:34Z)
• Multi-Stream Temporal Fusion for Financial Fraud Detection [0.0]
  Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.<n>We propose the Multi-Stream Fraud Transformer, a unified architecture that encodes each event stream with independent Transformer encoders.
  arXiv  Detail & Related papers  (2026-06-23T17:37:27Z)
• Safety-Aware Evaluation of LLM-Generated Driver Intervention Messages through Multi-Task Risk Fusion [0.0]
  General-purpose metrics such as BLEU and BERTScore cannot capture intervention-specific quality dimensions including risk-urgency alignment, cognitive load, and driver acceptability.<n>We propose the Driver Safety-Aware Intervention Score (DSAIS), a domain-specific metric evaluating five dimensions through a hybrid architecture.
  arXiv  Detail & Related papers  (2026-06-21T22:50:55Z)
• UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction [42.532335022354545]
  Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on singlemodality sensor data.<n>This work introduces UTOPYA, a multimodal framework that jointly addresses anomaly detection, time-series prediction, and phase classification in batch distillation.<n>A physics-informed regularisation scheme introduced in this work enforces temporal smoothness and thermodynamic monotonicity.
  arXiv  Detail & Related papers  (2026-05-18T10:28:15Z)
• Open-Set Vein Biometric Recognition with Deep Metric Learning [0.0]
  We rigorously evaluate the computational boundaries of Deep Metric Learning (DML) under strict open-set constraints.<n>Our approach learns discnative L2-normalised embeddings and employs prototype-based matching with a similarity threshold.<n>On the large-scale MMCBNU 6000 benchmark, our best model (ResNet50-CBAM) achieves an OSCR of 0.9945, AUROC of 0.9974, and EER of 1.57%, maintaining high identification accuracy (99.6% Rank-1) while robustly rejecting unknown subjects.
  arXiv  Detail & Related papers  (2026-04-16T11:03:11Z)
• How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles [46.63622714488747]
  Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement.<n>In practice, this manifests as correlated reasoning patterns and synchronized failures.<n>We develop a statistical framework for auditing behavioral entanglement among black-box language models.
  arXiv  Detail & Related papers  (2026-04-08T23:32:06Z)
• Tide: A Customisable Dataset Generator for Anti-Money Laundering Research [0.0]
  We present Tide, an open-source synthetic dataset generator.<n>It produces graph-based financial networks incorporating money laundering patterns.<n>Tide enables reproducible, customisable dataset generation tailored to specific research needs.
  arXiv  Detail & Related papers  (2026-03-02T13:44:18Z)
• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Trajectory Guard -- A Lightweight, Sequence-Aware Model for Real-Time Anomaly Detection in Agentic AI [0.0]
  Trajectory Guard is a Siamese Recurrent Autoencoder with a hybrid loss function that jointly learns task-trajectory alignment via contrastive learning and sequential validity via reconstruction.<n>At 32 ms latency, our approach runs 17$-27times$ faster than LLM Judge baselines, enabling real-time safety verification in production deployments.
  arXiv  Detail & Related papers  (2026-01-02T00:27:11Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Cross-LLM Generalization of Behavioral Backdoor Detection in AI Agent Supply Chains [0.0]
  We present the first systematic study of cross-LLM behavioral backdoor detection.<n>We show that single-model detectors achieve 92.7% accuracy within their training distribution but only 49.2% across different LLMs.<n>We show that model-aware detection model identity as an additional feature achieves 90.6% accuracy universally across all evaluated models.
  arXiv  Detail & Related papers  (2025-11-25T03:33:04Z)
• Adaptive Monitoring and Real-World Evaluation of Agentic AI Systems [3.215065407261898]
  Multi-agent systems that combine large language models with external tools are rapidly transitioning from research laboratories into high-stakes domains.<n>This "Advanced" sequel fills that gap by providing an algorithmic instantiation or empirical evidence.<n>AMDM cuts anomaly-detection latency from 12.3 s to 5.6 s on simulated goal drift and reduces false-positive rates from 4.5% to 0.9%.
  arXiv  Detail & Related papers  (2025-08-28T15:52:49Z)
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation [52.2244588424002]
  We present RoboTwin 2.0, a scalable framework for automated, large-scale generation of diverse and realistic data.<n>At its core is RoboTwin-OD, an object library of 731 instances across 147 categories with semantic and manipulation-relevant annotations.<n>To improve sim-to-real transfer, RoboTwin 2.0 applies structured domain randomization along five axes.
  arXiv  Detail & Related papers  (2025-06-22T16:26:53Z)
• Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
  Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents.<n>In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches.<n>We show that even moderate levels of information sharing significantly mitigate environment-specific errors.
  arXiv  Detail & Related papers  (2025-03-21T18:06:28Z)
• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure [52.2025114590481]
  We introduce Hybrid-Segmentor, an encoder-decoder based approach that is capable of extracting both fine-grained local and global crack features. This allows the model to improve its generalization capabilities in distinguish various type of shapes, surfaces and sizes of cracks. The proposed model outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status.
  arXiv  Detail & Related papers  (2024-09-04T16:47:16Z)
• Comparing Hyper-optimized Machine Learning Models for Predicting Efficiency Degradation in Organic Solar Cells [38.647921189039934]
  This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of organic solar cells (OSCs)<n>We generated a database with 996 entries, which includes up to 7 variables regarding both the manufacturing process and environmental conditions for more than 180 days.<n>The accuracy achieved reaches values of the coefficient determination (R2) widely exceeding 0.90, whereas the root mean squared error (RMSE), sum of squared error (SSE), and mean absolute error (MAE)>1% of the target value, the PCE.
  arXiv  Detail & Related papers  (2024-03-29T22:05:26Z)
• Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior [54.629850694790036]
  spectral-normalized identity priors (SNIP) is a structured pruning approach that penalizes an entire residual module in a Transformer model toward an identity mapping. We conduct experiments with BERT on 5 GLUE benchmark tasks to demonstrate that SNIP achieves effective pruning results while maintaining comparable performance.
  arXiv  Detail & Related papers  (2020-10-05T05:40:56Z)
• FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation [72.41370576242116]
  We propose a semi-supervised Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs. It consists of two main modules: pyramid correlation mapping and residual reconstruction. Experiment results show that the proposed scheme achieves the state-of-the-art performance, with improvement by 0.80, 1.15 and 0.10 in terms of average end-point error (AEE) against competing baseline methods.
  arXiv  Detail & Related papers  (2020-01-17T07:13:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.