Data challenges in wealth management has been added to your bookmarks.
Data challenges in wealth management
Capitalizing on the underlying opportunity
From increasing regulation to new digital delivery channels, shifting wealth demographics to fee pressure, and now cyber security threats, wealth managers continue to face a myriad of industry dynamics. To stay competitive and compliant, firms are embarking on new initiatives, such as rolling out alternative investment products and creating a single client identifier. These innovations increase data volumes and complexity, making it harder to manage, maintain, and mine data. Requirements for high-quality data availability have never been more pressing as wealth managers seek to expand globally, enhance sales effectiveness, and add robo-investing capabilities.
Leading wealth managers leverage data to
1. Take advantage of scale and global expansion
The opportunity: A number of wealth management firms have looked to go global and multi-regional in search of growth. Two benefits in addition to sheer size are:
- Easier to consistently serve clients domiciled in one region with investments, interests, or family members in another region—a growing situation especially among ultra-high net worth investors.
- Access to scale economies in technology (e.g. CRM and portfolio management tools, trading platforms), in processes (e.g. client on-boarding, reporting, account maintenance and execution), and the people needed to support them.
The challenges: Although these wealth management firms recognize the benefits of going global, they have encountered significant data challenges in achieving their global vision. The common themes are:
- "Defining" the client
- Local versus global frameworks
- Missing or incorrect data
- Data usage and accessibility
What to do: Most firms that have grappled with these challenges recognize that having a robust and flexible data management approach is a must.
How to do this: Many lessons can be learned from those who are well on their way to a robust data management capability, including:
- The organization needs to understand what the full capability means—it's not just about data quality or just about data governance.
- Implementing comprehensive data management needs to be phased in, even at the data element level.
- Facts need to be gathered to make a strong business case to build out the full capabilities.
- Support from the top is needed to ensure that both global and local needs are addressed.
2. Drive sales effectiveness and organic growth
The opportunity: Asset managers are facing industry shifts and associated operational challenges. More data (product, transaction, and customer-related) is available than ever before and new analytics capabilities enable asset managers to better identify and engage with clients.
The challenges: The increasing maturity of analytics capabilities and advances in data engineering create several challenges, including:
- Saturated product categories, limited differentiation and increasing focus on passive products
- Evolving preferences for sales engagement
- 360 view of the client
- Optimization of product-channel mixes
What to do: Wealth management organizations need to invest in modernizing data architectures to ingest and consume a variety of structured and unstructured data sources, ranging from real-time trading data to social media and sentiment data. This will not only require technology updates, but more importantly, an integrated data model will need to be defined by the business to logically combine and connect external market data with internal CRM, risk, and financial data.
How to do this: Holistically gather business reporting requirements and include internal and external wholesalers, data governance, and technology teams in the process:
- Get the business to drive the development of the logical and conceptual business model.
- Create an Enterprise Reporting Dictionary with a comprehensive set of measures, attributes, and metrics required to fulfill reporting needs.
- Establish an Architecture COE to ensure that solution architecture and design decisions are not made in siloes, with integration across functions being key.
- Adopt an agile approach to building reporting capabilities on top of the integrated model, starting with a small subset of critical KPIs and evolving multiple views based on regular end user feedback.
3. Enable digitization through robo-advice
The opportunity: The popularity of robo-advising has continued to increase within the wealth management industry, primarily driven by the availability of technology to enable customized solutions for customers at a cheaper price. Specifically, advances in big data and analytics have jumpstarted the proliferation of attempts to create robo-advising offerings for customers of wealth management firms. The development of these services started in automated portfolio allocation, and firms are looking to expand the scope of services to other areas such as goals-based advice, business succession planning, insurance, and other automation-enabled activities. This market is becoming quickly saturated with many players trying to take market share, and include not only traditional financial institutions but also smaller, individual-focused fintechs.
The challenge: Some key data challenges are:
- Automation limitations/account onboarding
- Account aggregation
- Single client identifier
- Goals-based models
- Social media integration
What to do: There are multiple approaches to incorporating a robo-advising component to the offerings at wealth management firms, each with their own data-specific requirements.
How to do this: Firms should choose the robo-advice model and capabilities that align with strategic objectives in order to choose the right path to market for this type of offering.