The wealth management industry is typically seen as embodying old-fashioned values and providing discrete, tailored services. These attributes remain valuable parts of the business, but for many clients, they are no longer sufficient. In a highly connected world, people want faster and more convenient offerings and a cutting-edge digital experience. Amid rising competition, established wealth managers need to keep pace with new offerings as they retain the values that set them apart.
Wealth managers are unlikely to be able to serve modern clients effectively without a digitized operating model. This will support advisory and non-advisory activities and service everchanging investment preferences. Some leading managers are building modular data and IT architectures, which enable smart decision-making, personalization at scale, and more extensive product offerings.
The changes are also helping them meet their regulatory obligations, boosting the productivity of relationship managers (RMs), and lifting compressed margins.
For wealth managers interested in pursuing these benefits, this article lays out the potential of deploying advanced analytics and offers a playbook of measures that wealth managers should consider including in a digital transformation.
The case for advanced analytics
Meeting the needs of today’s customers requires a business model that is at the same time efficient and adaptable to individual clients. Wealth managers are finding success with two approaches:
- Serve clients across the wealth continuum on a flat-fee advisory basis. Instead of the still-prevalent product-focused model, wealth managers need to build in pricing flexibility aligned to clients’ needs at every stage of their lives. An increasingly common pricing model is for clients to negotiate a flat fee based on the value of their investments. To maintain revenues with this model, wealth managers need to create new efficiencies and ensure RMs are more productive, which means spending more time with clients.
- Embrace personalization aligned to client life stages and goals. Today’s customers are increasingly dissatisfied with a one-size-fits-all service model, so wealth managers should consider transitioning to needs-based personalization. This requires RMs to get comfortable with a wider range of solutions, from the simplest products to complex higher-yielding investments (private markets, venture capital, pre-IPO, and structured products). In addition, RMs must be equipped to help clients make complex investment decisions, supported by analytics.
In today’s context, each of these goals is achievable only with advanced capabilities in data and analytics, especially targeting relationship management.
Focus on relationship management
Modernization can be game changing when it targets the role of RMs. Based on conversations with industry participants, we estimate that RMs typically spend 60 to 70 percent of their time on non-revenue-generating activities, amid rising regulatory and compliance obligations (Exhibit 1). One reason is that most still work with legacy IT systems or even spreadsheets. As clients demand more engagement and remote channel options, that needs to change.
Exhibit 1
A few leading wealth managers are using technology to provide RMs with the tools to serve clients more efficiently and effectively. Some have taken a zero-based approach, rebuilding their tech stacks and embracing advanced analytics to inform more personalized services. By providing targeted solutions, these firms have been able to boost revenues and reduce operational costs.
Clear benefits of being more client focused
The benefits of digitization are relevant in most markets, but the potential to leverage digitization to achieve a significant performance uplift is especially great in regions where wealth managers have not yet seized the opportunity. In Asia, for example, many wealth managers still need to fully embrace digital ways of working (Exhibit 2). We estimate that IT-based transformations could create some $40 billion to $45 billion of incremental value for wealth managers serving high-net-worth individuals in Asia, equating to roughly 25 basis points on a wealth pool of $17 trillion.
Exhibit 2
Drilling down in the potential gains from data and analytics, we see benefits in three key areas: acquisition and onboarding, engagement and deepening of client relationships, and servicing and retention.
Acquisition and onboarding. Basic acquisition and onboarding applications include client discovery, risk profiling, account opening, and onboarding. RMs and investment teams can use analytics for lead generation, share-of-wallet modeling, and automated proposals. There are also multiple applications in investment management, risk, and compliance, including social-profile checking, anti-money-laundering and know your customer, and fraud protection.
Engagement and deepening. Client-focused applications include personalized research, portfolio management, and notifications. RMs and investment teams can implement client clustering, propensity modeling, recommendation engines, and digital performance management (see sidebar “How analytics creates sustainable impact: Two examples from Asia”). In investment management, risk, and compliance, there are opportunities to de-bias investment decisions, data analysis, and trade execution.
Servicing and retention. Client-related applications include portfolio simulations and optimization, as well as self-execution of trades. RMs can leverage applications such as churn predictors and work planners, while investment management, risk, and compliance can scale up portfolio planning and trade surveillance.
A playbook for analytics-driven wealth management
Early success stories are encouraging, but they are the exception rather than the rule. More often, firms have started the transformation journey but have faltered along the way. Common reasons include a lack of ownership at senior levels and budgetary or strategic restraints that prevent project teams from executing effectively.
The challenges of transforming service models are significant but not insurmountable. Indeed, as analytics use cases become more pervasive, implementation at scale becomes more achievable. In the following paragraphs, we present five ingredients of an analytics-based transformation (Exhibit 3). These can be supported by strong leadership, a rigorous focus on outcomes, and a willingness to embrace new ways of working. Indeed, managers who execute effectively will get ahead of the competition and be much more adept in meeting client needs.
Exhibit 3
Set a leadership vision
Analytics-driven transformations are often restricted to narrow silos occupied by a few committed experts. As a result, applications fail to pick up enough momentum to make a real difference to performance. Conversely, if support for change programs comes from the top and is guided by an outcomes-driven approach, the business can break away from entrenched operating norms and reset for structural change. With that in mind, executive teams should communicate a vision that can be cascaded through the business. They should also create a safe environment, or sandbox, for business lines to experiment before scaling.
Plot the change journey
Wealth managers have applied advanced analytics to achieving different objectives. Some have found that the application of advanced analytics to business problems delivers significant value and enables them to make better decisions faster and more consistently. Others are using data and advanced analytics to improve sales and marketing, inform investment decision-making, and boost RM productivity.
Any plan for data-driven change must fit the organization’s business model. Implementation will vary based on the technical feasibility, data accuracy and accessibility, time to impact, scalability, and availability of funds. The first few use cases will set the mood and direction, so careful thought is required ahead of action.
One common impediment to scaling is the lack of a single metric to describe impact, which makes it hard for tech teams to communicate benefits. Still, there are workarounds. Financial key performance indicators (KPIs) can show flows across key mandates or volumes of advisory, rather than execution-driven assets under management. Nonfinancial metrics can focus on cross-sell ratios, increased client retention, number of RMs trained, or adoption rates for solutions. Other helpful evaluations include customer satisfaction scores, new trust-based RM-client relationships, time to market, and cultural shifts. Progress on these measures will boost organizational conviction that transformation is beneficial.
Build a strong foundation, leading with technology
Data and technology together form the backbone that supports analytics-led transformation. A strong analytics backbone requires a rigorous standard of data management, coupled with informed decisions about the IT applications and systems to employ.
Wealth managers are routinely in touch with their clients offline. These interactions elicit significant information about client preferences and requirements, but the information is often stored on paper or in RMs’ heads. To mine this knowledge fully, wealth managers must capture it digitally and convert it into a structured format that can be processed to create insights and personalized services (see sidebar “A digital approach to client-centric servicing”). In doing so, they need to put systems in place to ingest, store, and organize the data in line with regulatory obligations while ensuring the data are accurate, available, and accessible.
On the technology side, some leading wealth managers use natural-language processing to analyze text and voice data and identify personalized triggers and insights. Others are building feedback loops across channels to train artificial intelligence algorithms. Technologies can also be applied to processes: robotic process automation, for example, can replace routine manual labor and mental processing in regulatory compliance, risk assessment, reporting, and query management.
Deployment of data-driven decision-making requires scalable, adaptable, and resilient core technology components—a unified data and technology stack that connects across IT activities.
This will enable managers to adopt a tech-first approach to designing customer journeys.
In building data and IT architecture, wealth managers require a basic tool kit with four key components:
- a rationalized IT stack to create a common front-and back-end platform and a unified resource for mobile and web applications
- a scalable data platform with modular data pipelines and application-programming-interface (API)-based microservices for building and deploying analytics solutions at scale
- a semi-autonomous lab environment to enable experimentation, coupled with an at-scale factory environment for production of analytics solutions
- a highly scalable distributed network on the cloud to respond to variable demand for data storage and processing
In parallel to assembling these components, banks must consolidate data from across geographies and business lines. This will enable analysts to elicit insights based on the maximum amount of information. Some leading players first experiment in a sandbox environment and work with external partners to acquire the necessary skills, after which they scale up incrementally.
Build the team and prioritize change management
It is not easy to scale and sustain analytics impact. Organizational silos and cultural resistance are common inhibiting factors, while the vital role that RMs play in forming and maintaining relationships must be adapted to the new environment. Indeed, RMs must be front and center of the transformation process. For this, organizations need effective team building and change management.
Team building. A productive approach to team building is to create cross-functional squads with a range of talents (Exhibit 4). Product owners and designers should be responsible for ensuring that the team meets the needs of its clients (RMs or end clients) and stays focused on delivering value. Data scientists and data engineers implement use cases and check that insights are generated as data are ingested—a minimal-viable-product (MVP) approach. IT architects and software engineers, meanwhile, build the slick interfaces and back-end systems that deliver insights to clients across channels.
Exhibit 4
A core objective should be to explore analytics and AI use cases that boost RM productivity (see sidebar “How three Asian wealth managers engaged clients and boosted RM productivity”). To that end, the squad should embed business and channel management teams so that ideas are aligned with RM client services. Several firms have found that involving RMs and other domain experts in squads leads to significant improvements in data interpretation and modeling.
In many cases, assembling productive squads will require new talent. In particular, banks will need data scientists to be responsible for building analytics software and data engineers to scope and build data pipelines and data architecture. Translators, who act as conduits between the business and technology teams, will be critical for ensuring that squads understand business needs. Finally, squads need IT skill sets to ensure that analytics and digital solutions are compatible with core data and technology stacks.
The best approach to talent acquisition is to take baby steps: get one squad right, foster RM adoption, and then gradually expand capabilities as use cases multiply and are scaled up. Some of the required skill sets are in high demand, so outsourcing may be a realistic early option. In the longer term, however, it makes sense to build internally.
Change management. Relationship managers should be encouraged to embrace analytics and convinced that new applications lead to better services and higher levels of performance. Change management strategies can help. Examples include creating teams of “influencers,” running capability-building sessions, developing change narratives that generate widespread excitement, redefining roles, and aligning performance with financial or nonfinancial awards.
Institutionalize new ways of working
Analytics-driven transformation at scale should be predicated on collaboration, team self-steering, and an iterative approach to problem-solving—elements of the so-called agile approach, which originated in software development. In running agile sprints, it pays to keep business needs in sight, accepting that failure is part of the process. Two-week sprints are usually sufficient to get pilots up and running, and the aim should be to produce an MVP with every sprint.
Wealth managers can apply these basic principles via four process disciplines:
- Inspect and adapt. Daily check-ins will ensure that teams identify roadblocks, such as product backlogs, and maintain their focus on goals.
- Engage end users. Sprint reviews with end users, stakeholders, and sponsors enable teams to gather feedback and bake in recommendations.
- Embed a sense of unity and purpose. Teams should hold retrospectives to incorporate learnings.
- Institutionalize support infrastructure. Agile tooling (for example, Confluence, Jira, and Zeplin) will facilitate experimentation and support remote working where necessary.
Organizations using agile operating models must embrace flexible learning. This is a departure from traditional waterfall-based approaches, in which decision-making occurs at the beginning of each project. In agile, capability building and a relentless focus on change management will be vital elements of optimizing the program. To cement the relationship between innovation and growth, leading firms also assign KPIs to application rollouts, and they reward decision makers based on the value created.
Most wealth managers would say they have already embarked on an analytics journey; many have begun deploying digital applications in various aspects of their businesses. Often, however, the whole system is less than the sum of its parts, and people remain attached to established ways of working. To make a leap forward, wealth managers should commit to bold agendas that will support the scaling up of analytics-driven approaches.
