Applying Fine-Tuning methods to FTTransfomer in Anti Money Laundering applications

Bachelor thesis (2024)

Authors

V.P. de Graaff Electrical Engineering, Mathematics and Computer Science

Contributors

T.A. Akyıldız Data-Intensive Systems - EEMCS (supervisor 2)
Kubilay Atasu Data-Intensive Systems - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
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Published Date

27-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

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.