Evaluating goal-advice appropriateness for personal financial advice

Abstract

Over the years, the number of consumers seeking personal financial advisory services has grown globally. However, recent studies indicate a worrying decline in consumers' 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, time-consuming 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.