Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions
We introduced a multimodal foundational model for financial transactions that integrates both structured attributes and unstructured textual descriptions into a...
Abstract
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
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Citation
Gustavo Polleti. "Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions." arXiv preprint. 2025-11-15. http://arxiv.org/abs/2511.12154v1
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References
- [1]ResearchCredibility: 9/10Gustavo Polleti. "Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions." arXiv.org. November 15, 2025. Accessed November 18, 2025.
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Original Source
This article is based on Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions (arXiv.org)
