The business of wealth management has gone through enormous changes over the last 20 years, and technology has played a major role in helping to keep up with those changes. For example, there have been various models of wealth management, from main street investors picking individual stocks on their own, to the rise of traditional money management programs that use financial advisors or third-party money managers.

In the last several years, regulatory mandates and the evolution in the understanding of portfolio management theories have led the industry to move from assuring the suitability and risk to looking at an investor's investment goals and objectives when making portfolio and asset modeling decisions. Technology has played an important role in this process.

Technology strategy has become an increasingly important part of the overall business strategy, largely due to several emerging trends. Cheap hardware and open source software has allowed for the rapid construction and deployment of cloud platforms. Micro services and container architectures have let us build processes that can be rapidly adapted to a changing business landscape. Big data analytics allows us to better understand prospects, build client relationships, and leverage those insights in our business decisions. Machine learning has the potential to provide both operational efficiencies and to assist in the portfolio decision process.

Data and analytics, in particular, are becoming critical to strategic business decisions and digital marketing. Structured and unstructured data can help answer many questions about wealth management prospects and clients: What are our prospects thinking about? What are they interested in? What kind of demographics do our prospects and clients represent? Can we target these demographic or regional segments through marketing or sales? What other market segments are we interested in reaching? We can identify what objectives are important to them, whether there are broad goals or themes among them, and whether there are any missed opportunities in the client experience journey.

Our client relationships require us to answer a number of questions across a vast span of data points. How many times did clients login to the client websites and mobile applications? How many times did they call a help desk with an issue or a question? How often did they reach out to a banker or a wealth manager? What sentiments can we capture from the client interactions?