Evaluating goaladvice appropriateness for personal financial advice Over the years, the number of consumers seeking personal financial advisory services has grown globally. However, recent studies indicate a worrying decline in consumersxe2x80x99 trust and confidence in advisers and financial institutions, as well as low regulatory compliance rates. Inspiring consumer trust through increased vigilance of advice is not possible using current auditing practices as reviews are manual, timeconsuming and complex. In this paper, we describe a generalised framework which leverages machine learning approaches to systematically characterise the risk status of financial advice documents prior to client delivery. We show how the framework presented provides a comprehensive, accurate and efficient compliance review of financial advice documents for financial advisers and compliance officers alike.

Evaluation of Bias in Sensitive Personal Information Used to Train Financial Models Bias in data can have unintended consequences which propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted modellifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current stateofpractice.