Embracing advanced AI/ML to help investors achieve success: Vanguard Reinforcement Learning for Financial Goal Planning

TL;DR

This paper explores the application of deep reinforcement learning in financial goal planning, enabling personalized investment strategies by modeling complex market behaviors to improve investor success.

Contribution

It introduces a novel reinforcement learning algorithm for financial planning that models investor trajectories as a Markov decision process for personalized savings strategies.

Findings

Reinforcement learning effectively models complex financial behaviors.

The algorithm identifies optimal savings rates across multiple goals.

Enhanced personalization improves financial planning outcomes.

Abstract

In the world of advice and financial planning, there is seldom one right answer. While traditional algorithms have been successful in solving linear problems, its success often depends on choosing the right features from a dataset, which can be a challenge for nuanced financial planning scenarios. Reinforcement learning is a machine learning approach that can be employed with complex data sets where picking the right features can be nearly impossible. In this paper, we will explore the use of machine learning for financial forecasting, predicting economic indicators, and creating a savings strategy. Vanguard ML algorithm for goals-based financial planning is based on deep reinforcement learning that identifies optimal savings rates across multiple goals and sources of income to help clients achieve financial success. Vanguard learning algorithms are trained to identify market indicators and behaviors too complex to capture with formulas and rules, instead, it works to model the financial success trajectory of investors and their investment outcomes as a Markov decision process. We believe that reinforcement learning can be used to create value for advisors and end-investors, creating efficiency, more personalized plans, and data to enable customized solutions.