Author: Denis Avetisyan
A novel federated learning framework allows financial institutions to share insights and improve anti-money laundering detection without compromising data privacy.
This paper presents FedGraph-VASP, a privacy-preserving system leveraging graph neural networks and post-quantum cryptography for secure cross-institutional analysis.
Detecting cross-institutional money laundering presents a fundamental tension between regulatory compliance and user privacy. To address this, we introduce ‘FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering’, a federated learning framework that enables collaborative anti-money laundering detection by exchanging compressed graph embeddings secured with post-quantum cryptography. Our experiments demonstrate that this approach outperforms state-of-the-art generative baselines in connected transaction graphs, achieving a 12.1 percent improvement in F1-score on a Bitcoin dataset. However, the effectiveness of embedding exchange versus generative imputation appears topology-dependent; can we develop hybrid approaches that optimally leverage both techniques across diverse financial networks?
Navigating the Labyrinth: The Evolving Challenge of Illicit Finance
Contemporary financial crime increasingly relies on techniques designed to evade detection by established anti-money laundering (AML) systems. A particularly challenging tactic is “chain-hopping,” wherein illicit funds are rapidly moved through a complex network of accounts and shell companies, deliberately obscuring their original source and beneficial owner. This method bypasses traditional transaction monitoring, which often focuses on identifying large, singular movements of money or transfers to known high-risk jurisdictions. By fragmenting funds into smaller amounts and routing them through seemingly legitimate, but ultimately connected, entities, chain-hopping effectively creates a smokescreen, making it incredibly difficult for investigators to trace the funds back to their criminal origins and hindering efforts to disrupt the flow of illicit capital. The speed and intricacy of these modern techniques demand a reevaluation of current AML strategies and the development of more advanced analytical tools capable of identifying these complex patterns of financial manipulation.
Tracing the complex pathways of illicit financial flows demands a level of visibility currently hampered by data siloing. While individual institutions possess fragments of information regarding suspicious transactions, a comprehensive understanding requires secure and effective data sharing between them. However, this presents a considerable paradox: the very data crucial for identifying and preventing financial crime is inherently sensitive and protected by stringent privacy regulations. Balancing the need for cross-institutional collaboration with the imperative to safeguard personal and financial data necessitates innovative approaches, such as privacy-enhancing technologies like federated learning and secure multi-party computation. These techniques aim to enable collaborative analysis without directly exposing underlying data, offering a potential pathway towards more robust anti-money laundering systems without compromising individual privacy rights. The challenge lies not merely in technical implementation, but also in establishing the necessary legal frameworks and trust amongst institutions to facilitate this secure data exchange.
Current financial crime detection systems frequently encounter a fundamental trade-off between identifying illicit transactions and safeguarding the privacy of legitimate financial data. Traditional rule-based approaches, while simple to implement, often generate a high number of false positives, requiring extensive manual review and potentially infringing on individual privacy. More sophisticated machine learning models, capable of improved accuracy, demand access to vast datasets, raising concerns about data security and the potential for misuse. This dilemma is further complicated by evolving privacy regulations, such as GDPR, which restrict the collection and processing of personal financial information. Consequently, financial institutions grapple with the challenge of building effective defenses against money laundering and terrorist financing without unduly compromising the confidentiality of their customers’ financial lives, necessitating innovative solutions that prioritize both security and privacy.
A Collaborative Shield: Introducing FedGraph-VASP
FedGraph-VASP is a federated learning framework developed to identify illicit transactions occurring across multiple Virtual Asset Service Providers (VASPs). This system enables collaborative fraud detection without requiring the direct exchange of sensitive transaction data between VASPs. Instead, each VASP trains a local model on its own data, and only model updates – not the underlying transaction records – are shared with a central server for aggregation. This approach preserves data privacy while leveraging the combined knowledge of multiple VASPs to improve the accuracy of illicit transaction detection. The framework is designed to address the challenges of fragmented data and regulatory concerns within the virtual asset ecosystem.
FedGraph-VASP leverages Graph Neural Networks (GNNs) to analyze the inherent structure of financial transaction networks. GNNs operate directly on graph-structured data, allowing the system to identify complex relationships and patterns indicative of illicit activity that would be difficult to detect through traditional methods. These networks learn node embeddings – vector representations of each entity (e.g., user, account) – based on both node features and the network’s topology. By propagating information across the graph, GNNs can effectively capture contextual information and identify anomalous behavior based on deviations from established patterns, facilitating the detection of fraudulent transactions and money laundering schemes.
FedGraph-VASP utilizes a federated learning approach to mitigate data privacy concerns inherent in collaborative fraud detection. This methodology allows Virtual Asset Service Providers (VASPs) to train a shared global model without directly exchanging sensitive transaction data; instead, only model updates are communicated. This preserves the confidentiality of individual VASPs and their customer information while still enabling the identification of illicit financial activities. Performance evaluation indicates the system achieves an F1-score of 0.508 in detecting fraudulent transactions under this privacy-preserving framework.
Securing the Network: Enabling Safe Cross-Institutional Communication
Boundary Embedding Exchange enables Virtual Asset Service Providers (VASPs) to securely share account information necessary for cross-institutional transactions without revealing Personally Identifiable Information (PII). This is achieved by transmitting compressed, non-invertible representations – termed “embeddings” – of accounts. The process intentionally eliminates the possibility of reconstructing the original account details from the exchanged embedding, thus protecting sensitive data. This approach facilitates compliance with privacy regulations while still enabling essential functionalities such as transaction monitoring and risk assessment across different VASPs. The size of these embeddings is optimized to minimize transmission overhead while retaining sufficient information for the intended security and compliance purposes.
To protect the exchange of account embeddings from future threats posed by quantum computing, the system employs a Post-Quantum Cryptography scheme. This scheme utilizes the Kyber-512 key encapsulation mechanism alongside AES-256-GCM for symmetric encryption of the embedding data. Implementation of this cryptographic layer introduces a measured encryption overhead of 0.10 milliseconds per embedding exchanged, representing the computational cost associated with ensuring long-term security against evolving quantum decryption capabilities. This approach aims to maintain confidentiality and integrity of the data even in a post-quantum computing landscape.
Performance evaluation of the system utilizes simulation of multi-VASP environments achieved through graph partitioning algorithms. Specifically, Louvain Community Detection is employed to identify community structure within transaction networks, while METIS Partitioning is used to divide the resulting graph into subsets for distributed processing and scalability testing. The Elliptic Bitcoin Dataset serves as the basis for these simulations, providing a realistic representation of transaction patterns and volumes. This methodology allows for assessment of system performance under conditions mirroring real-world, complex financial networks and enables benchmarking of scalability and efficiency metrics.
Balancing Detection and Privacy: Assessing System Robustness and Impact
To preserve the confidentiality of user data during graph analysis, the system leverages Private Set Intersection (PSI). This cryptographic technique allows the identification of common connections – shared boundary nodes between accounts – without exposing the complete lists of accounts themselves. Essentially, PSI enables a determination of which accounts have a relationship without revealing all accounts involved in that relationship. This is achieved through a secure exchange of data where only the intersecting elements are revealed, effectively masking the non-shared account identifiers and bolstering the overall privacy of the system. The result is a robust method for detecting illicit financial activity while minimizing the risk of exposing sensitive user information.
Even with the implementation of privacy-preserving techniques, the system remains vulnerable to specific adversarial attacks requiring diligent mitigation strategies. Evaluations revealed a concerning susceptibility to both Embedding Inversion Attacks and Membership Inference Attacks; the Embedding Inversion Attack, which attempts to reconstruct sensitive input data from the learned embeddings, achieved an R^2 score of 0.32, indicating a moderate ability to recover information. More critically, the Membership Inference Attack, designed to determine if a particular data point was used in the training process, demonstrated a high Area Under the Curve (AUC) of 0.95, suggesting a substantial risk of revealing individual participation in the system. These findings highlight the necessity of continuous security assessments and the development of robust defenses against these evolving threats to safeguard user privacy and maintain the integrity of the platform.
FedGraph-VASP presents a viable approach to identifying illicit financial activity within the rapidly changing sphere of virtual assets by carefully calibrating the trade-off between accurate detection and strong privacy safeguards. This system doesn’t simply prioritize uncovering suspicious transactions; it does so while minimizing the exposure of sensitive user data. Demonstrating a significant advancement over the FedSage+ baseline – achieving a 12.1% performance increase and an F1-score of 0.508 – FedGraph-VASP establishes itself as a practical and scalable solution. This improved performance indicates a more robust ability to distinguish between legitimate and illicit activity, offering a crucial tool for financial institutions and regulatory bodies seeking to combat financial crime in the digital age without compromising individual privacy.
The development of FedGraph-VASP exemplifies the principle that structure dictates behavior within complex systems. This framework isn’t merely about combining data; it’s about architecting a collaborative intelligence that respects institutional boundaries while fostering a holistic view of financial networks. The secure exchange of compressed graph embeddings, a key component of this system, demonstrates how a carefully designed structure-in this case, the federated learning process and post-quantum cryptography-can fundamentally alter the behavior of anti-money laundering detection. As Edsger W. Dijkstra once stated, “It is a great mistake to think that one can program a computer to do something it doesn’t understand.” Similarly, FedGraph-VASP doesn’t simply apply machine learning; it structures the learning process itself to address the inherent complexities and privacy concerns of cross-institutional data analysis, proving that clear ideas, not just computational power, truly scale.
What’s Next?
The architecture presented here, while addressing critical concerns of data privacy and emerging quantum threats, implicitly highlights the fragility of trust in distributed systems. FedGraph-VASP operates on the premise of coordinated action, yet offers little in the way of formal verification regarding the integrity of participating institutions. If the system survives on duct tape-compressing embeddings and hoping for the best-it’s probably overengineered. The true challenge isn’t merely exchanging information, but establishing a reliable framework for assessing the trustworthiness of that information’s origin.
Furthermore, the emphasis on graph embeddings, while effective for knowledge transfer, risks obscuring the underlying complexity of financial networks. Modularity without context is an illusion of control. Future work should explore methods for preserving-and communicating-the provenance of data within these embeddings, allowing for a more nuanced understanding of risk and a better means of detecting adversarial manipulation. Simply put, a compressed signal is easily lost in the noise.
Ultimately, the success of federated learning in this domain hinges not on algorithmic refinement, but on the development of robust governance structures. The framework necessitates a shift in perspective: from protecting data within institutions, to protecting the integrity of the system itself. It’s a subtle distinction, but one that will likely define the next generation of anti-money laundering technologies.
Original article: https://arxiv.org/pdf/2601.17935.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-01-28 02:20