Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow

Cyrile Delestre; Yoann Sola

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arXiv · 2410.08243

Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow

Cyrile Delestre·Yoann Sola

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Abstract

Banking Transaction Flow (BTF) is a sequential data found in a number of banking activities such as marketing, credit risk or banking fraud. It is a multimodal data composed of three modalities: a date, a numerical value and a wording. We propose in this work an application of self-attention mechanism to the processing of BTFs. We trained two general models on a large amount of BTFs in a self-supervised way: one RNN-based model and one Transformer-based model. We proposed a specific tokenization in order to be able to process BTFs. The performance of these two models was evaluated on two banking downstream tasks: a transaction categorization task and a credit risk task. The results show that fine-tuning these two pre-trained models allowed to perform better than the state-of-the-art approaches for both tasks.

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Cyrile Delestre, Yoann Sola. 2024-10-10. Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow. https://arxiv.org/abs/2410.08243

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