Picture this: a recently retired client with 1.3 million dollars in investable assets comes to you and asks how they should manage their money. You would need to think of how to best strategically suggest what to save, what to invest, how much to invest, and when to do so. This is the day in the life of a wealth manager.
The field of wealth management deals with advising affluent clients on how to best invest, manage, and grow their money and assets. Wealth management is beyond financial advising, as it takes into account a client’s holistic financial stretch including planning for retirement, estate management, tax services, and more.
After the 2009 global financial crisis, wealth managers reassessed their services to become more cautious and calculative in their strategic approach. On par with the changing needs of customers, it became clear that the wealth management industry needed some remodeling.
Traditionally, wealth management services have concentrated on the use of reporting and management information system (MIS) capabilities. These tools monitor a client’s financial performance to make sure they are optimal by mitigating risk, organizing data flows, and evaluating business processes against projected ones. To get maximum value from big data, machine learning is also being utilized to a greater extent than ever before by predicting customer behavior insights and saving time. Specifically, natural language processing can aid in assessing value in divesting or investing through sentimental analysis, text extraction, and text classification. To further strengthen the wealth advisory process, managers are now looking to combine predictive and descriptive strategies: being able to rely on data amid uncertainty and better utilizing customer response. This would ultimately support coming up with industry-specific wealth management analytics, which allows for monitoring activity to adjust companies’ responses in real time. All of these analytic tools combined are creating a wider digital infrastructure that will help wealth management business models thrive.
Specifically, natural language processing can aid in assessing value in divesting or investing through sentimental analysis, text extraction, and text classification.
The newer generation is also looking for slightly different standards in wealth management advising than what was expected in previous generations. The current investors, Generation X and Y, vary from their predecessors in that they are more independent and see advisors as facilitators. They place more importance on genuinely understanding the reasoning behind advice being told to them. Instead of sitting on the guidelines and letting an advisor take control while turning a blind eye, these investors want to be in control of the decisions being made about their finances. The new generation of investors also has a negative view of risk. Before, managers combatted this through portfolio diversification, but more significance is placed on hedging strategies, methods used to prevent the loss of money when the avoidance of risk is crucial to the newer investors. These are just a few of the general patterns of thinking that make up future investors’ notions, and wealth-management firms must keep up and tailor their services to accommodate this.
The current investors, Generation X and Y, vary from their predecessors in that they are more independent and see advisors as facilitators.
The wealth management industry is also seeing more democratization: it’s not just for high-net-worth individuals anymore. More and more of the general public feels entitled to receive the same resources and join in on this disruptive evolution. Recently, numerous wealth management startups have come into the picture and have begun to expand on the strategies of established wealth management firms. As these startups are the first ever to tend to the needs of the middle class, their strategies have been built off of those for high-net-worth individuals and altered in many ways. Some of these firms allow individuals to diversify their portfolios into asset classes while others allow them to interact with the analytics directly and test their own hypotheses as investors do.
This up-and-coming industry continues to see demand, and wealth managers are gradually incorporating more technology and digitalization into their strategic outlook. The “avatar” or emotional aspect of a client, though, continues to be taken into account and will always play an important role, no matter how reliant wealth management services become on computers. Until then, wealth managers and investors should expect to glide with the technology wave.