Wealth Management (WM) today is undergoing an unprecedented transformation. A convergence of various structural changes has fundamentally altered the way private client managers cater to high net worth individuals (HNWIs), ultra high net worth individuals (HNWIs) and the mass affluent.
First, there is an ongoing demographic shift in the U.S., wherein millennials will overtake Baby Boomers as the biggest generation, and the share of assets controlled by women will soon hit an all-time high. The huge inter-generational wealth transfer that will occur over the coming decade has major implications for wealth managers, in terms of upending many existing client-advisor relationships and throwing up opportunities for new entrants to grab market share.
Second, clients’ expectations and preferences around services and fees are changing rapidly, particularly with regard to millennial investors who want portfolio transparency and control, and press for performance-linked management pay. The new generation of private clients is also seeking personalized solutions and digital tools mirroring their evolving life goals and aspirations–spanning areas including estate and succession planning, retirement and leisure.
Third, the industry now has to contend with increasingly onerous regulations concerning fiduciary responsibility, fraud prevention and other mandates, resulting in a greater focus on compliance. Over 50% of wealth managers anticipate an acceleration in regulatory changes over the next three years, a report published in mid-2018 by WealthBriefing showed.
So how can wealth managers effectively respond to these significant changes, and generate superior returns for clients in a challenging macro environment of persisting uncertainty and rising costs of risk? Wealth managers will do well to take a leaf out of the book of banks and insurers, who are leveraging various disruptive technologies to reimagine their operating and business models.
Big Data Analytics
Investment firms now have a golden opportunity to harness data-driven insights for making many of their core functions including sales, advisory, customer engagement, portfolio management and risk mitigation more agile, efficient and effective. In fact, nearly 75% of wealth managers intend to increase their adoption of Big Data analytics, according to Boston Consulting Group.
With advanced data analytics tools, wealth managers can assess clients’ historical investing trends, transactions, behaviour patterns and other parameters in depth. Accordingly, they can determine the most apt products and services for individual customers, in alignment with clients’ risk tolerance and investment style. By anticipating each client’s expectations or responses in different situations, they can come up with relevant recommendations, in turn boosting up-selling and cross-selling.
Also, portfolio managers aiming to generate alpha can use data analytics to exhaustively scan various alternative data sources such as social media, online reviews, geolocation and satellite imagery to unearth relevant insights.
Artificial Intelligence and Machine Learning
A number of wealth managers are feeling their way around artificial intelligence (AI) and machine learning (ML). Some of them have begun deploying self-learning algorithms for automating asset allocation, and rolling out “robo-advisory” services. These automated digital money management-cum-financial planning platforms help clients craft personalized portfolios in alignment with their respective goals and risk appetite.
Some private client managers, meanwhile, are harnessing AI to track account holders’ investing and spending habits for building dedicated financial literacy modules, and providing tailored recommendations on tax compliance, portfolio restructuring, etc.
That’s not all. Many industry players have started experimenting with AI tools to glean insights from unstructured data that is captured in CRM tools, client meeting notes and conversations, email correspondence, and so on. Such tools carry out natural language processing, text analysis and sentiment analysis to help wealth managers better understand each client’s risk tolerance, anticipate potential churn, and recommend next-best offers.
On the actual investing front too, algorithms are delivering tangible value. Given that historical data does not factor in fundamental securities analysis and subjective, futuristic opinions, some wealth managers are now using ML to reduce the individual biases of their financial advisors. With ML multiple structured and unstructured data sets including communication between portfolio managers can be mined to uncover negative cognitive biases that affect investing decisions.
Robotic process automation (RPA) is another emerging disruptive technology that can increase the operational efficiency of wealth managers. Many of the repetitive, labor-intensive tasks and business processes can be automated using RPA, thereby eliminating human errors, and freeing up advisors to focus on portfolio management and customer engagement. These tasks and workflows include client onboarding, trade processing, account rebalancing reconciliation, and financial planning, among others.
Wealth managers can also reimagine customer relationship management (CRM) in the digital era by embracing next-generation engagement tools and channels, and data analysis techniques. For example, some leading U.S. banks are building dedicated tools for their advisors to smoothen client onboarding and portfolio management. Others are revamping their CRM setups to ensure compliance with changing regulations concerning client data privacy and confidentiality in different jurisdictions.
As financially savvy clients increasingly demand the same kind of digital experiences available to them in media, retail and other industries, wealth managers must provision on-demand, easily accessible, intuitive engagement channels. These channels should deliver a comprehensive view of the client’s finances and portfolio, besides empowering them with self-service tools for moving and managing money.
Some industry players have relaunched their client portals, and rebuilt or augmented existing mobile apps for enhanced security and functionalities, while some are developing creative vlogs to reach out to customers during market volatility.
Wealth managers weighing a modernization of their IT architecture, applications and infrastructure must remember that any such transformation must eventually provision different functionalities in a flexible, open, and standardized manner.
The next-generation platform should be based on the microservices software framework, wherein big, complex IT systems are broken up into different easily manageable and autonomous components, each of which solves a specific business problem. A major benefit of using microservices–small, flexible and self-contained software modules that can execute a particular task on its own–is that they do not require recoding of existing applications. Feature enhancements can be incorporated by simply adding new independent components that sync with existing ones.
Through microservices-based IT transformation, wealth managers will be able to roll out new services faster, while refining current ones, thus ensuring quick, seamless and cost-efficient upgrade and maintenance.
Wealth managers must urgently revamp their operating and business models for the digital era, as clients seek both human and automated advisory that is oriented toward goal-based planning and outcome-oriented investing. Leveraging next-generation technologies such as AI, ML, RPA and Big Data analytics will help wealth managers survive and thrive in this new landscape, and strengthen their competitive advantage.
For faster and higher ROI, the industry should partner with specialized vendors having in-depth domain expertise and a proven track record of consulting and executing mission-critical IT transformations. We, at Damco, have worked with several wealth managers over the years, successfully delivering on architecture and platform modernization projects, as well as CRM overhaul and process revamp initiatives.