@inproceedings{faria-de-azevedo-etal-2024-bbrc,
    title = "{BBRC}: {B}razilian Banking Regulation Corpora",
    author = "Faria de Azevedo, Rafael  and
      Eduardo Muniz, Thiago Henrique  and
      Pimentel, Claudio  and
      Jose de Assis Foureaux, Guilherme  and
      Caldeira Macedo, Barbara  and
      Vasconcelos, Daniel de Lima",
    editor = "Chen, Chung-Chi  and
      Liu, Xiaomo  and
      Hahn, Udo  and
      Nourbakhsh, Armineh  and
      Ma, Zhiqiang  and
      Smiley, Charese  and
      Hoste, Veronique  and
      Das, Sanjiv Ranjan  and
      Li, Manling  and
      Ghassemi, Mohammad  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.finnlp-1.15",
    pages = "150--166",
    abstract = "We present BBRC, a collection of 25 corpus of banking regulatory risk from different departments of Banco do Brasil (BB). These are individual corpus about investments, insurance, human resources, security, technology, treasury, loans, accounting, fraud, credit cards, payment methods, agribusiness, risks, etc. They were annotated in binary form by experts indicating whether each regulatory document contains regulatory risk that may require changes to products, processes, services, and channels of a bank department or not. The corpora in Portuguese contain documents from 26 Brazilian regulatory authorities in the financial sector. In total, there are 61,650 annotated documents, mostly between half and three pages long. The corpora belong to a Natural Language Processing (NLP) application that has been in production since 2020. In this work, we also performed binary classification benchmarks with some of the corpus. Experiments were carried out with different sampling techniques and in one of them we sought to solve an intraclass imbalance problem present in each corpus of the corpora. For the benchmarks, we used the following classifiers: Multinomial Naive Bayes, Random Forest, SVM, XGBoost, and BERTimbau (a version of BERT for Portuguese). The BBRC can be downloaded through a link in the article.",
}

We present BBRC, a collection of 25 corpus of banking regulatory risk from different departments of Banco do Brasil (BB). These are individual corpus about investments, insurance, human resources, security, technology, treasury, loans, accounting, fraud, credit cards, payment methods, agribusiness, risks, etc. They were annotated in binary form by experts indicating whether each regulatory document contains regulatory risk that may require changes to products, processes, services, and channels of a bank department or not. The corpora in Portuguese contain documents from 26 Brazilian regulatory authorities in the financial sector. In total, there are 61,650 annotated documents, mostly between half and three pages long. The corpora belong to a Natural Language Processing (NLP) application that has been in production since 2020. In this work, we also performed binary classification benchmarks with some of the corpus. Experiments were carried out with different sampling techniques and in one of them we sought to solve an intraclass imbalance problem present in each corpus of the corpora. For the benchmarks, we used the following classifiers: Multinomial Naive Bayes, Random Forest, SVM, XGBoost, and BERTimbau (a version of BERT for Portuguese). The BBRC can be downloaded through a link in the article.
Faria de Azevedo, Rafael Eduardo Muniz, Thiago Henrique Pimentel, Claudio Jose de Assis Foureaux, Guilherme Caldeira Macedo, Barbara Vasconcelos, Daniel de Lima
BBRC: Brazilian Banking Regulation Corpora