The wealth management industry has always been an embodiment of data crunching. During the periods of recessions, it has become more so as data can bring in the requisite foresightedness. Since then, owing to the growing regulatory requirements and the fast evolution of technologies, the industry has been in constant flux.
That apart, the market dynamics are also changing. Due to the rise of the new world order post the 2009 crash, there has been a rise in the HNIs and UHNIs in the Asian tiger economies. The new clients come with their unique and diverse set of requirements. This puts additional pressure on the wealth managers. They need to tailor the products as per the new requirements and also search for newer commodities to invest in.
Let us look at a few use cases where Data Science can play a major role in making wealth management more data-driven and risk-proof.
Risk Management: This word is synonymous with the wealth management industry. Clients are advised to spend their savings on stocks and shares of other companies. The UHNIs and HNIs want their risks to be covered and also get ensured return on investment. Predictive analytics can study past trends and historical data to analyze how a particular stock/ share it going to behave in the future. This predicted future value can be an essential criterion for the buy or leave of the decision-making process.
Compliance: As already stated, there has been a lot of compliance and regulatory pressure on the wealth management post the economic depression, to curb the anarchy that prevailed till then. That apart, there are other regulatory requirements such as GDPR to comply with. It would have been one painstaking process if the new tools and technologies for effective data management hadn’t been made available to them. These tools and technologies help in data masking and data anonymization.
Workflow Management and Process Automation: Needless to say, many organizations are under tremendous pressure to cut down costs and reduce their operational overheads. The wealth management companies are no exception. They are also exploring Robotic Process Automation (RPA) to streamline their workflows and automate a majority of the redundant tasks. Of late, their use cases are further expanding beyond the rudimentary check clearance and helpdesk activities. RPA and NLP are being used to identify anomalies and also monitor the dashboards alerting the stakeholders whenever something goes beyond the marked threshold. This particular attribute is coming in handy in curbing fraudulent activities too.
Segmentation and Targeting: The number the billionaires from the Asian tiger’s economies has been on the rise in recent years, and it will continue doing so. Data Science helps in the behavioral segmentation of the clients. Data can provide insights into the most probable targets and low hanging fruits. It can also help identify the customers who are most probably going to jump the ship. By segmentation and proper targeting of the clients, one can easily have targeted campaigns that will reduce the operational costs of the organizations. This becomes the direct feed for the algorithms which are being leveraged to improved sales productivity.
Investment portfolios are also sometimes given the shape of a product, which is something akin to the mutual funds. Given the algorithms, the products are also tailored to suit the UHNIs, and HNIs needs better.
Research: When the wealth managers walk into the office of a client, they need to have their research and homework handy. This needs a lot of effort. Even though they have access to data, it is lying discretely in a different repository. Data analytics makes it easier to ingest data into one common pool. From there, NLP-based research can let the wealth managers have a view of the data they have and do the needful analysis. The rise of data visualization tools in the market can help the wealth managers look into the data in a visual format, which further assists in their decision-making process.
Asset management: It is the core of all the services provided by wealth management firms. Analytics can provide a view as to which ones are being profitable, which one needs to be let go of, and are their new products which can be added to the kitty. Data analytics, driven by the market sentiments and powered by forecasting engines, can also find out newer assets, which have been traditionally being ignored until now.
Movies like the Big Shot have already exemplified how data analytics can help make wealth management more foolproof. There are other organizational areas like workforce management and customer analytics, where they are being utilized inside the organization. We have also seen that data science helps wealth management companies make the operations leaner while complying with the regulatory requirements.