Vd
V.P. de Graaff
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1
This research investigates the effectiveness of combining Feature Tokenizer Transformer (FTTransformer)[6] with graph neural networks for anti-money laundering (AML) applications. We explore various fine-tuning techniques, including LoRA[9] and vanilla fine-tuning, on our baseline FTT architecture. Using the IBM AML dataset [1], we compare the performance of different models and fine-tuning approaches. Our results indicate that FTT alone do not outperform GNN’s and careful configuration is required when working with datasets of Multi-Modality. This work contributes to the development of more efficient and accurate methods for detecting financial fraud patterns.
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This research investigates the effectiveness of combining Feature Tokenizer Transformer (FTTransformer)[6] with graph neural networks for anti-money laundering (AML) applications. We explore various fine-tuning techniques, including LoRA[9] and vanilla fine-tuning, on our baseline FTT architecture. Using the IBM AML dataset [1], we compare the performance of different models and fine-tuning approaches. Our results indicate that FTT alone do not outperform GNN’s and careful configuration is required when working with datasets of Multi-Modality. This work contributes to the development of more efficient and accurate methods for detecting financial fraud patterns.
This research investigates the effectiveness of combining Feature Tokenizer Transformer (FTTransformer)[6] with graph neural networks for anti-money laundering (AML) applications. We explore various fine-tuning techniques, including LoRA[9] and vanilla fine-tuning, our baseline FTT architecture. Using the IBM AML dataset [1], we compare the performance of different models and fine-tuning approaches. Our results indicate that FTT alone do not outperform GNN’s and careful configuration is required when working with datasets of Multi-Modality. This work contributes to the development of more efficient and accurate methods for detecting financial fraud patterns.
...
This research investigates the effectiveness of combining Feature Tokenizer Transformer (FTTransformer)[6] with graph neural networks for anti-money laundering (AML) applications. We explore various fine-tuning techniques, including LoRA[9] and vanilla fine-tuning, our baseline FTT architecture. Using the IBM AML dataset [1], we compare the performance of different models and fine-tuning approaches. Our results indicate that FTT alone do not outperform GNN’s and careful configuration is required when working with datasets of Multi-Modality. This work contributes to the development of more efficient and accurate methods for detecting financial fraud patterns.