Summary of Self-attention Mechanism in Multimodal Context For Banking Transaction Flow, by Cyrile Delestre et al.
Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow
by Cyrile Delestre, Yoann Sola
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an application of self-attention mechanisms to process Banking Transaction Flows (BTFs), a type of sequential data used in marketing, credit risk, and banking fraud. Two general models, one RNN-based and one Transformer-based, are trained on a large amount of BTFs in a self-supervised manner. A specific tokenization approach is introduced to process BTFs. The models’ performance is evaluated on two banking downstream tasks: transaction categorization and credit risk prediction. Fine-tuning the pre-trained models outperforms state-of-the-art approaches for both tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special data called Banking Transaction Flows, which are used in banks to understand transactions better. They created two machine learning models that can process this type of data without needing labeled training examples. These models were tested on two important banking tasks: categorizing transactions and predicting credit risk. The results show that these models did a better job than other approaches for both tasks. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Rnn » Self attention » Self supervised » Tokenization » Transformer
