Delivering tailored financial solutions through hyper-personalization enabled by machine learning algorithms

Abstract

Emphasizing hyper-personalization enabled by Artificial Intelligence (AI) algorithms that drive meaningful engagement with the clients in the production of state-of-the-art investment strategies and advice to leverage the sensitivity of client portfolios to systematic risks – provides asset management firms with potentially unprecedented differentiation opportunities. However, practical implementation challenges require execution through a multi-faceted approach – enhancing back-end data, systems infrastructure, and data science capabilities; re-purposing client engagement models and front-end tools; refining talent requirements, workflows, and processes; as well as addressing important ethical considerations regarding data usage, advice quality, and client privacy. Such differentiation initiatives can be fueled by embedding lower-cost AI-driven data collection and processing techniques that channel and sort the digital exhaust created by clients in their high-frequency interactions with the broader world – and tie them to actionable dynamic financial strategies that aim to translate the AI-enhanced understanding of their motivations and triggers into timely portfolio repositionings to protect the upside and downside for both aggressive and defensive investors during “normal” and “bad” market regimes respectively.