Assessing Regulatory Risk in Personal Financial Advice Documents: a Pilot Study
Abstract
Assessing regulatory compliance of personal financial advice is currently a complex manual process. In Australia, only 5%- 15% of advice documents are audited annually and 75% of these are found to be non-compliant(ASI 2018b). This paper describes a pilot with an Australian government regulation agency where Artificial Intelligence (AI) models based on techniques such natural language processing (NLP), machine learning and deep learning were developed to methodically characterise the regulatory risk status of personal financial advice documents. The solution provides traffic light rating of advice documents for various risk factors enabling comprehensive coverage of documents in the review and allowing rapid identification of documents that are at high risk of non-compliance with government regulations. This pilot serves as a case study of public-private partnership in developing AI systems for government and public sector.
Keywords
Cite
@article{arxiv.1910.12580,
title = {Assessing Regulatory Risk in Personal Financial Advice Documents: a Pilot Study},
author = {Wanita Sherchan and Simon Harris and Sue Ann Chen and Nebula Alam and Khoi-Nguyen Tran and Adam J. Makarucha and Christopher J. Butler},
journal= {arXiv preprint arXiv:1910.12580},
year = {2019}
}
Comments
Presented at AAAI FSS-19: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA