How to tap into the wealth transfer: The power of predictive analytics

Insights

• The "Great Wealth Transfer" refers to the anticipated transfer of trillions of dollars from baby boomers to younger generations, primarily millennials and Generation Z, over the next few decades.
• As baby boomers age and pass away, their significant wealth will shift to heirs and charities, creating opportunities for wealth managers to engage with a new client base.
• Millennials and Gen Z have distinct expectations for wealth management, prioritizing digital accessibility, transparency, and personalized services over traditional banking relationships.
• Wealth managers must adapt their strategies to meet the technological and service expectations of younger clients or risk losing them to more innovative competitors.
• The integration of predictive analytics and data-driven insights can enhance personalized financial services, helping wealth managers anticipate clients' evolving needs and preferences.

A new era of wealth management

The “great wealth transfer,” or the anticipated handoff of trillions of dollars from older generations, particularly baby boomers, to their heirs and charities, is underway. This transfer could involve more than $120 trillion over the next 25 years, with a significant portion coming from high-net-worth households. Baby boomers control a substantial share of household wealth, but as this generation ages and dies, their assets will shift to younger generations.

As baby boomers begin to pass down their wealth, this presents a significant opportunity for wealth managers to engage with millennials and Generation Z, who are expected to inherit these assets. However, these younger generations have distinct expectations and preferences that challenge traditional wealth management practices.

Millennials and Gen Z are characterized by their digital-first approach and values-driven mindsets. A recent study showed that over 90% of millennials prefer mobile apps for banking and financial services and expect similar accessibility from their asset managers. These younger generations prioritize transparency, ease of use, and personalization in financial services. One survey showed that 58% of millennials are likely to switch financial providers to one that offers more personalized services.

Unlike their predecessors, millennials and Gen Z are less likely to remain loyal to traditional institutions simply due to legacy relationships. They expect wealth managers to utilize technology effectively and provide seamless digital experiences. Wealth managers must adapt to these expectations or risk losing clients to more technologically adept competitors.

Predictive analytics: Data-driven relationships in wealth management

The shift necessitates a modern approach to asset management, leveraging new technologies like predictive analytics to create personalized portfolios that resonate with younger clients. Predictive analytics helps asset managers anticipate life events or changes in risk appetite, enabling proactive outreach and engagement. This is crucial for retaining clients as their financial needs evolve. With access to data-driven insights, wealth managers can quickly analyze vast amounts of data, providing actionable insights that can enhance decision-making processes.

Despite the potential benefits of predictive analytics in asset and wealth management, several challenges hinder its adoption:

1. Data bias: If the datasets used for training predictive models contain implicit or explicit biases, the outputs will reflect those biases, potentially leading to skewed results. For instance, if historical data disproportionately represents certain demographics, the model might not perform well for underrepresented groups.
2. Data privacy: Compliance with data protection regulations such as the EU’s GDPR is critical. Wealth managers must prioritize client confidentiality and consent before utilizing client data for model training. Failure to do so can result in legal repercussions and loss of trust.
3. Overreliance on models: While predictive models can analyze vast amounts of data and generate actionable insights, overdependence on these models can be detrimental. Wealth managers must balance data-driven insights with human judgment to make informed decisions.

While predictive analytics offers powerful insights and the potential to transform wealth management, it is not a panacea. The effectiveness of these models hinges on various factors, and there are specific conditions under which predictive analytics could falter. Understanding these limitations is crucial for wealth managers seeking to leverage data-driven strategies. By recognizing the scenarios where predictive analytics can underperform, financial services organizations can mitigate risks and enhance the reliability of their analytical frameworks.

1. Build and forget: Models require continuous testing and updates to remain relevant. Without continuous alignment with current data patterns, model effectiveness diminishes over time.
2. Poor data quality or availability: The success of analytics models hinges on underlying data quality. Poorly maintained data can lead to inaccurate predictions and misguided strategies.
3. Reduced stakeholder involvement: Lack of oversight from business teams during model development can lead to misaligned objectives. Involving stakeholders ensures that models are relevant and actionable.

Embracing predictive analytics

The global market for predictive analytics is projected to grow significantly — from approximately $18 billion today to $250 billion by 2037 at a compound annual growth rate of 22.5%, with financial services being the largest user of predictive tools. Wealth managers must adopt a data-driven mindset, supported by robust technology infrastructure to capitalize on this trend.

Recommendations for effective adoption

To successfully integrate predictive analytics into their operations, wealth managers should consider the following strategies:

1. Data-driven mindset: Prioritize unbiased and accurate data collection to ensure reliable outputs from predictive models.
2. Pilot projects: Initiate proof-of-concept projects to experiment with different analytics models and determine which yields the best outcomes for specific challenges.
3. Regular adjustment: Monitor models for necessary adjustments based on changing inputs or market conditions.
4. Human knowledge: Ensure that results from analytics models are reviewed by functional experts who can validate suggestions for practical implementation.
5. Involve key stakeholders: Engage stakeholders throughout the model development process to align their objectives and expectations.
6. Foster cross-functional collaboration: Encourage collaboration between different departments within the organization to enhance knowledge sharing and innovation.
7. Embrace advanced analytics tools: Invest in tools that facilitate deeper insights and more effective decision-making processes.
8. Focus on client needs: Tailor services based on clients’ specific needs and preferences, ensuring a personalized approach that resonates with younger generations.
9. Utilize high-quality data: Ensure all models are built on robust datasets that accurately reflect market conditions.

The unprecedented generational wealth transfer presents a unique opportunity for wealth managers. Those willing to adapt their strategies in response to changing client expectations have a higher chance of retaining the next generation of clientele and can better connect with them. Focusing on data-driven decision-making and innovation in building lasting relationships will also attract new clients.