How Machine Learning Helps Wealth Managers Deepen Client Relationships

First, if you combine what you already know about your clients with data on the products and services each client has, then you can use machine learning to build a predictor of need. For example, if clients A, B, and C all have a need for product X, and client D looks very similar to clients A, B, and C, then it's a good bet that client D needs product X, too. This is a classic supervised learning problem: You train an algorithm based on known outcomes (clients A, B, and C have product X), and then use the resulting model to predict the need for product X among the clients who do not have that product.