  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.
