Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents

Wanita Sherchan, Sue Ann Chen, Simon Harris, Nebula Alam, Khoi-Nguyen Tran, Christopher J. Butler

AAAI 2020 pp. 13636-13637

doi:10.1609/AAAI.V34I09.7105 /aaai/2020/sherchan2020aaai-cognitive/

Abstract

This paper describes Cognitive Compliance - a solution that automates the complex manual process of assessing regulatory compliance of personal financial advice. The solution uses natural language processing (NLP), machine learning and deep learning to characterise the regulatory risk status of personal financial advice documents with traffic light rating for various risk factors. This enables comprehensive coverage of the review and rapid identification of documents at high risk of non-compliance with government regulations.

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Cite

Text

Sherchan et al. "Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I09.7105

Markdown

[Sherchan et al. "Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/sherchan2020aaai-cognitive/) doi:10.1609/AAAI.V34I09.7105

BibTeX

@inproceedings{sherchan2020aaai-cognitive,
  title     = {{Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents}},
  author    = {Sherchan, Wanita and Chen, Sue Ann and Harris, Simon and Alam, Nebula and Tran, Khoi-Nguyen and Butler, Christopher J.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13636-13637},
  doi       = {10.1609/AAAI.V34I09.7105},
  url       = {https://mlanthology.org/aaai/2020/sherchan2020aaai-cognitive/}
}