Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders

• URL: http://arxiv.org/abs/2601.21446v1
• Date: Thu, 29 Jan 2026 09:25:13 GMT
• Title: Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders
• Authors: Francesco Zola, Lucia Muñoz, Andrea Venturi, Amaia Gil,
• Abstract summary: Illicit financial activities often manifest through recurrent topological patterns in transaction networks.<n>We investigate whether Graph Autoencoders (GAEs) can effectively learn and distinguish topological patterns that mimic money laundering operations when trained on synthetic data.
• Score: 0.9869634509510014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Illicit financial activities such as money laundering often manifest through recurrent topological patterns in transaction networks. Detecting these patterns automatically remains challenging due to the scarcity of labeled real-world data and strict privacy constraints. To address this, we investigate whether Graph Autoencoders (GAEs) can effectively learn and distinguish topological patterns that mimic money laundering operations when trained on synthetic data. The analysis consists of two phases: (i) data generation, where synthetic samples are created for seven well-known illicit activity patterns using parametrized generators that preserve structural consistency while introducing realistic variability; and (ii) model training and validation, where separate GAEs are trained on each pattern without explicit labels, relying solely on reconstruction error as an indicator of learned structure. We compare three GAE implementations based on three distinct convolutional layers: Graph Convolutional (GAE-GCN), GraphSAGE (GAE-SAGE), and Graph Attention Network (GAE-GAT). Experimental results show that GAE-GCN achieves the most consistent reconstruction performance across patterns, while GAE-SAGE and GAE-GAT exhibit competitive results only in few specific patterns. These findings suggest that graph-based representation learning on synthetic data provides a viable path toward developing AI-driven tools for detecting illicit behaviors, overcoming the limitations of financial datasets.

Related papers

• OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection [48.77471686671269]
  OWLEYE is a novel framework that learns transferable patterns of normal behavior from multiple graphs.<n>We show that OWLEYE achieves superior performance and generalizability compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-01-27T02:08:18Z)
• A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs [0.9199488958939334]
  The rise of digital ecosystems has exposed the financial sector to evolving abuse and criminal tactics.<n>Traditional rule-based systems lack the adaptability needed to detect sophisticated or coordinated criminal behaviors.<n>We propose an approach that integrates graph machine learning and network analysis to improve the detection of well-known topological patterns.
  arXiv  Detail & Related papers  (2025-09-16T06:43:11Z)
• CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision [11.139587480845144]
  We propose Context Refactoring Contrast (CRoC), a framework that trains Graph Neural Networks (GNNs) for Graph Anomaly Detection (GAD)<n>CRoC exploits the class imbalance inherent in GAD to leverage limited labeled and abundant unlabeled data.<n>In the training stage, CRoC is further integrated with the contrastive learning paradigm. This allows GNNs to effectively harness unlabeled data during training, producing richer, more discnative node embeddings.
  arXiv  Detail & Related papers  (2025-08-17T08:05:17Z)
• PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems [10.160654114774513]
  Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats (APTs)<n>We introduce PROVSYN, an automated framework that synthesizes provenance graphs through a three-phase pipeline.<n>We show that PROVSYN produces high-fidelity graphs and improves detection performance through effective data augmentation.
  arXiv  Detail & Related papers  (2025-06-06T16:41:17Z)
• Structural Alignment Improves Graph Test-Time Adaptation [17.564393890432193]
  We introduce Test-Time Structural Alignment (TSA), a novel algorithm for Graph Test-Time Adaptation (GTTA)<n>TSA aligns graph structures during inference without accessing the source data.<n>Experiments on synthetic and real-world datasets demonstrate TSA's consistent outperformance of both non-graph TTA methods and state-of-the-art GTTA baselines.
  arXiv  Detail & Related papers  (2025-02-25T16:26:25Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• Graph Structure Refinement with Energy-based Contrastive Learning [56.957793274727514]
  We introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation.<n>We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR.<n>ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T04:05:09Z)
• GADY: Unsupervised Anomaly Detection on Dynamic Graphs [18.1896489628884]
  We propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets.
  arXiv  Detail & Related papers  (2023-10-25T05:27:45Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Graph Generative Model for Benchmarking Graph Neural Networks [73.11514658000547]
  We introduce a novel graph generative model that learns and reproduces the distribution of real-world graphs in a privacy-controlled way. Our model can successfully generate privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to benchmark GNN models.
  arXiv  Detail & Related papers  (2022-07-10T06:42:02Z)
• Embedding Graph Auto-Encoder for Graph Clustering [90.8576971748142]
  Graph auto-encoder (GAE) models are based on semi-supervised graph convolution networks (GCN) We design a specific GAE-based model for graph clustering to be consistent with the theory, namely Embedding Graph Auto-Encoder (EGAE) EGAE consists of one encoder and dual decoders.
  arXiv  Detail & Related papers  (2020-02-20T09:53:28Z)

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.