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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.12154 |
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| _version_ | 1866909905206640640 |
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| author | Polleti, Gustavo Santana, Marlesson Fontes, Eduardo |
| author_facet | Polleti, Gustavo Santana, Marlesson Fontes, Eduardo |
| contents | We introduced a multimodal foundational model for financial transactions that integrates both structured attributes and unstructured textual descriptions into a unified representation. By adapting masked language modeling to transaction sequences, we demonstrated that our approach not only outperforms classical feature engineering and discrete event sequence methods but is also particularly effective in data-scarce Open Banking scenarios. To our knowledge, this is the first large-scale study across thousands of financial institutions in North America, providing evidence that multimodal representations can generalize across geographies and institutions. These results highlight the potential of self-supervised models to advance financial applications ranging from fraud prevention and credit risk to customer insights |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions Polleti, Gustavo Santana, Marlesson Fontes, Eduardo Machine Learning Artificial Intelligence We introduced a multimodal foundational model for financial transactions that integrates both structured attributes and unstructured textual descriptions into a unified representation. By adapting masked language modeling to transaction sequences, we demonstrated that our approach not only outperforms classical feature engineering and discrete event sequence methods but is also particularly effective in data-scarce Open Banking scenarios. To our knowledge, this is the first large-scale study across thousands of financial institutions in North America, providing evidence that multimodal representations can generalize across geographies and institutions. These results highlight the potential of self-supervised models to advance financial applications ranging from fraud prevention and credit risk to customer insights |
| title | Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.12154 |