Summary of Multimodal Banking Dataset: Understanding Client Needs Through Event Sequences, by Mollaev Dzhambulat et al.
Multimodal Banking Dataset: Understanding Client Needs through Event Sequences
by Mollaev Dzhambulat, Alexander Kostin, Postnova Maria, Ivan Karpukhin, Ivan A Kireev, Gleb Gusev, Andrey Savchenko
First submitted to arxiv on: 26 Sep 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 introduces an industrial-scale publicly available multimodal banking dataset, MBD, which contains over 1.5M corporate clients with multiple modalities including bank transactions, geo position events, dialogue embeddings, and product purchases. The dataset is anonymized from real proprietary bank data and can be used for large-scale multimodal algorithm development. The authors also introduce a novel benchmark with two business tasks: campaigning (purchase prediction) and matching of clients. They demonstrate the superiority of multi-modal baselines over single-modal techniques for each task, highlighting the potential for practical applications in event sequence analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shares a big dataset that can help machines learn from lots of data about people’s financial habits. The data is special because it comes from different sources like bank transactions and phone location information. This makes it hard to study because there isn’t much publicly available data like this. The researchers make their own dataset public so others can use it to develop new machine learning models that can help banks understand customer behavior better. |
Keywords
» Artificial intelligence » Machine learning » Multi modal
