HB
H.Ç. Bilgi
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
6 records found
1
Detecting money laundering in financial transaction data is a task where graph neural networks (GNNs) have shown strong potential. Such data is naturally represented as a directed multigraph, since two accounts, each represented as a node, may exchange many separate payments, each forming a distinct edge with its own amount, currency, and timestamp. Preserving these parallel edges, rather than collapsing them into a single connection, retains the fine-grained structure that allows for distinguishing laundering behaviour from ordinary activity. Yet these models also introduce a new vulnerability, as an adversary could manipulate the transaction graph to alter the neighbourhood of a suspicious account such that the GNN misclassifies it as benign. Existing adversarial robustness research operates on the adjacency matrix, which records at most one edge per node pair and therefore cannot represent the parallel transactions between two accounts that this task depends on. Multigraph GNNs therefore lack both a framework for evaluating robustness under structural perturbations and defences against such perturbations.
This thesis extends adversarial robustness analysis to multigraph GNNs through three contributions. First, it reformulates GNN message passing and attack optimisation over the incidence matrix instead of the adjacency matrix, yielding the first gradient-based structural attack that retains multi-edge structure. Second, it introduces unnoticeability loss terms that constrain perturbations to maintain the graph's statistical fingerprint, including the frequency of characteristic patterns such as short transaction cycles, keeping the attack statistically plausible and unnoticeable at the macro level. Third, it scales the framework to large networks with projected randomised block coordinate descent. On the IBM synthetic anti-money laundering dataset, learned attacks substantially reduce detection accuracy compared to non-learnable perturbations, and adversarial training recovers robustness, showing that multigraph GNNs are both vulnerable to structural manipulation and defensible against it. ...
This thesis extends adversarial robustness analysis to multigraph GNNs through three contributions. First, it reformulates GNN message passing and attack optimisation over the incidence matrix instead of the adjacency matrix, yielding the first gradient-based structural attack that retains multi-edge structure. Second, it introduces unnoticeability loss terms that constrain perturbations to maintain the graph's statistical fingerprint, including the frequency of characteristic patterns such as short transaction cycles, keeping the attack statistically plausible and unnoticeable at the macro level. Third, it scales the framework to large networks with projected randomised block coordinate descent. On the IBM synthetic anti-money laundering dataset, learned attacks substantially reduce detection accuracy compared to non-learnable perturbations, and adversarial training recovers robustness, showing that multigraph GNNs are both vulnerable to structural manipulation and defensible against it. ...
Detecting money laundering in financial transaction data is a task where graph neural networks (GNNs) have shown strong potential. Such data is naturally represented as a directed multigraph, since two accounts, each represented as a node, may exchange many separate payments, each forming a distinct edge with its own amount, currency, and timestamp. Preserving these parallel edges, rather than collapsing them into a single connection, retains the fine-grained structure that allows for distinguishing laundering behaviour from ordinary activity. Yet these models also introduce a new vulnerability, as an adversary could manipulate the transaction graph to alter the neighbourhood of a suspicious account such that the GNN misclassifies it as benign. Existing adversarial robustness research operates on the adjacency matrix, which records at most one edge per node pair and therefore cannot represent the parallel transactions between two accounts that this task depends on. Multigraph GNNs therefore lack both a framework for evaluating robustness under structural perturbations and defences against such perturbations.
This thesis extends adversarial robustness analysis to multigraph GNNs through three contributions. First, it reformulates GNN message passing and attack optimisation over the incidence matrix instead of the adjacency matrix, yielding the first gradient-based structural attack that retains multi-edge structure. Second, it introduces unnoticeability loss terms that constrain perturbations to maintain the graph's statistical fingerprint, including the frequency of characteristic patterns such as short transaction cycles, keeping the attack statistically plausible and unnoticeable at the macro level. Third, it scales the framework to large networks with projected randomised block coordinate descent. On the IBM synthetic anti-money laundering dataset, learned attacks substantially reduce detection accuracy compared to non-learnable perturbations, and adversarial training recovers robustness, showing that multigraph GNNs are both vulnerable to structural manipulation and defensible against it.
This thesis extends adversarial robustness analysis to multigraph GNNs through three contributions. First, it reformulates GNN message passing and attack optimisation over the incidence matrix instead of the adjacency matrix, yielding the first gradient-based structural attack that retains multi-edge structure. Second, it introduces unnoticeability loss terms that constrain perturbations to maintain the graph's statistical fingerprint, including the frequency of characteristic patterns such as short transaction cycles, keeping the attack statistically plausible and unnoticeable at the macro level. Third, it scales the framework to large networks with projected randomised block coordinate descent. On the IBM synthetic anti-money laundering dataset, learned attacks substantially reduce detection accuracy compared to non-learnable perturbations, and adversarial training recovers robustness, showing that multigraph GNNs are both vulnerable to structural manipulation and defensible against it.
Heterogeneous datasets hold a large percentage of all digital data that is available. With the rise of the digital medium, they have played a strong part in addressing the need for a structured way of storing data, particularly through the use of relational databases. To better leverage such data, the consensus of researchers has been in favour of using Graph Neural Networks to make predictions and infer pos- sible outcomes. With the rise of the Transformer model and the clear limitations that GNNs inherently have due to their over-smoothing and over-squashing properties, a clear transition occurred into combining and leveraging the properties of both GNNs and Transformer models, Graph Transformers. While this method has been better researched in the context of homogeneous datasets, much remains unexplored in its heterogeneous counterpart. This paper tackles this by applying Graph Transformers to multiple heterogeneous datasets, examining the differences and advantages between interleaved and cascade architectures of GTs and how the homogeneous positional encodings transfer to the heterogeneous context.
...
Heterogeneous datasets hold a large percentage of all digital data that is available. With the rise of the digital medium, they have played a strong part in addressing the need for a structured way of storing data, particularly through the use of relational databases. To better leverage such data, the consensus of researchers has been in favour of using Graph Neural Networks to make predictions and infer pos- sible outcomes. With the rise of the Transformer model and the clear limitations that GNNs inherently have due to their over-smoothing and over-squashing properties, a clear transition occurred into combining and leveraging the properties of both GNNs and Transformer models, Graph Transformers. While this method has been better researched in the context of homogeneous datasets, much remains unexplored in its heterogeneous counterpart. This paper tackles this by applying Graph Transformers to multiple heterogeneous datasets, examining the differences and advantages between interleaved and cascade architectures of GTs and how the homogeneous positional encodings transfer to the heterogeneous context.
Graph Transformers have played a key role in the latest graph learning developments. However, their application and performance in Relational Deep Learning (RDL), which has huge potential to remove inefficient data pre-processing pipelines, remain largely unexplored. For this reason, we present adaptations to two well-known Graph Transformer models: a relation-aware local message passing variant (FraudGT) that computes separate attention matrices for each edge and node type; and a simplified global-attention version that ignores heterogeneity (Graphormer). Our analysis demonstrates that local relation-aware attention achieves state-of-the-art results on node classification and regression tasks when evaluated against RelBench tasks, a set of comprehensive RDL benchmarks. We show how local message passing is computationally cheaper, faster, more efficient and more accurate than global attention. Our code is available at https://github.com/ignaciocunado/gt-rdl.
...
Graph Transformers have played a key role in the latest graph learning developments. However, their application and performance in Relational Deep Learning (RDL), which has huge potential to remove inefficient data pre-processing pipelines, remain largely unexplored. For this reason, we present adaptations to two well-known Graph Transformer models: a relation-aware local message passing variant (FraudGT) that computes separate attention matrices for each edge and node type; and a simplified global-attention version that ignores heterogeneity (Graphormer). Our analysis demonstrates that local relation-aware attention achieves state-of-the-art results on node classification and regression tasks when evaluated against RelBench tasks, a set of comprehensive RDL benchmarks. We show how local message passing is computationally cheaper, faster, more efficient and more accurate than global attention. Our code is available at https://github.com/ignaciocunado/gt-rdl.
Graph Learning on Financial Tabular Data
Cascade and Interleaved architectures using GNNs and Transformers
Detecting money-laundering activity in financial transactions is challenging due to the multigraph nature of the problem as well as the intricate fraud patterns that exist. In this work we introduce two architectures, Cascade and Interleaved. These architectures combine the expressive power of local message passing (MP) from Graph Neural Networks (GNNs) with the one of global message passing from Transformers. Both models leverage the Principal Neighborhood Aggregation (PNA) GNN for capturing rich local structure. We also incorporate the MEGA two-stage aggregation scheme to distinguish transactions that have the same source and destination accounts from other transactions. We further enhance our architectures with PEARL, a learnable positional encoding framework that has a reduced overhead compared to other techniques. We evaluate our models on the IBM transactions for Anti-Money Laundering (AML) synthetic datasets. We achieve significant improvements compared to the PNA baseline, and come close to tie SOTA results, while requiring less feature engineering on the input graphs and also show that the application of learnable positional encodings in financial fraud detection tasks is promising.
...
Detecting money-laundering activity in financial transactions is challenging due to the multigraph nature of the problem as well as the intricate fraud patterns that exist. In this work we introduce two architectures, Cascade and Interleaved. These architectures combine the expressive power of local message passing (MP) from Graph Neural Networks (GNNs) with the one of global message passing from Transformers. Both models leverage the Principal Neighborhood Aggregation (PNA) GNN for capturing rich local structure. We also incorporate the MEGA two-stage aggregation scheme to distinguish transactions that have the same source and destination accounts from other transactions. We further enhance our architectures with PEARL, a learnable positional encoding framework that has a reduced overhead compared to other techniques. We evaluate our models on the IBM transactions for Anti-Money Laundering (AML) synthetic datasets. We achieve significant improvements compared to the PNA baseline, and come close to tie SOTA results, while requiring less feature engineering on the input graphs and also show that the application of learnable positional encodings in financial fraud detection tasks is promising.
Graph Learning on Tabular Data: Think Global And Local
Full Fusion and Interleaved architectures on IBM’s Anti-Money Laundering Data
As financial fraud becomes increasingly sophisticated, traditional detection methods struggle to uncover the complex relational patterns underlying illicit behavior. This paper investigates the effectiveness of combining Graph Neural Networks (GNNs) and Transformers for fraud detection on relational data transformed into graph structures. Focusing on the IBM Anti-Money Laundering (AML) dataset, two hybrid architectures are proposed: Interleaved, which alternates between GNNs and Transformers to exploit local and global information sequentially, and Full-Fusion, which fuses parallel GNN and Transformer representations at both feature and decision levels. The results show that integrating Transformers significantly boosts performance over standalone GNN baselines, with improvements up to 10% in the F1 score in small-scale datasets. It is also demonstrated that gating-based fusion strategies enhance model stability and accuracy, and further, that PEARL-based positional encodings do not result in any conclusive improvement of the models. These findings highlight the value of combining local message passing and global attention mechanisms for structured financial anomaly detection, and pave the way for more robust, adaptable graph-based solutions in fraud analytics and more.
...
As financial fraud becomes increasingly sophisticated, traditional detection methods struggle to uncover the complex relational patterns underlying illicit behavior. This paper investigates the effectiveness of combining Graph Neural Networks (GNNs) and Transformers for fraud detection on relational data transformed into graph structures. Focusing on the IBM Anti-Money Laundering (AML) dataset, two hybrid architectures are proposed: Interleaved, which alternates between GNNs and Transformers to exploit local and global information sequentially, and Full-Fusion, which fuses parallel GNN and Transformer representations at both feature and decision levels. The results show that integrating Transformers significantly boosts performance over standalone GNN baselines, with improvements up to 10% in the F1 score in small-scale datasets. It is also demonstrated that gating-based fusion strategies enhance model stability and accuracy, and further, that PEARL-based positional encodings do not result in any conclusive improvement of the models. These findings highlight the value of combining local message passing and global attention mechanisms for structured financial anomaly detection, and pave the way for more robust, adaptable graph-based solutions in fraud analytics and more.
Money laundering detection stands as one of the most important challenges in the anti-financial crime sector, given its grave repercussions on the financial industry. The evolving nature of fraud schemes and the increasing volume of financial transactions impose limitations on the detection capabilities of traditional anti-money laundering (AML) systems. In the light of the recent breakthroughs in the field of graph machine learning, graph neural networks (GNNs) and graph transformers (GTs) have emerged as prominent solutions to these limitations, achieving a remarkable performance in detecting complex and broad fraudulent patterns. However, fusing the powerful characteristics of these classes of graph models into a unified framework for fraud detection has been little explored. In this paper, we address this gap by presenting GraphFuse — a hybrid graph representation learning model tailored for money laundering detection in financial transaction graphs. The novel edge centrality and transaction signature encodings offer GraphFuse a slight advantage over the best-performing GNN and GT models, improving upon the best GT baseline by 0.76 p.p. in F1 score. Additionally, we introduce three variants of the Transformer-based component of GraphFuse, each with a different level of computational complexity. The competitive performance of Graph-Fuse is supported by extensive experiments on open-source, large-scale synthetic financial transactions datasets. Our code is available at https://github.com/mfrija/aml-graphfuse.
...
Money laundering detection stands as one of the most important challenges in the anti-financial crime sector, given its grave repercussions on the financial industry. The evolving nature of fraud schemes and the increasing volume of financial transactions impose limitations on the detection capabilities of traditional anti-money laundering (AML) systems. In the light of the recent breakthroughs in the field of graph machine learning, graph neural networks (GNNs) and graph transformers (GTs) have emerged as prominent solutions to these limitations, achieving a remarkable performance in detecting complex and broad fraudulent patterns. However, fusing the powerful characteristics of these classes of graph models into a unified framework for fraud detection has been little explored. In this paper, we address this gap by presenting GraphFuse — a hybrid graph representation learning model tailored for money laundering detection in financial transaction graphs. The novel edge centrality and transaction signature encodings offer GraphFuse a slight advantage over the best-performing GNN and GT models, improving upon the best GT baseline by 0.76 p.p. in F1 score. Additionally, we introduce three variants of the Transformer-based component of GraphFuse, each with a different level of computational complexity. The competitive performance of Graph-Fuse is supported by extensive experiments on open-source, large-scale synthetic financial transactions datasets. Our code is available at https://github.com/mfrija/aml-graphfuse.