HB
H.Ç. Bilgi
5 records found
1
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 l
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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 rea
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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 det
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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 expre
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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 th
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