The State of Today: Uneven Distribution Prioritizing Pivotal AI Use Cases within Wealth Management

May 2024

Straight To The Point

Inspired by science fiction author, William Gibson, the sub-title of this piece references a quote which alludes to the belief that some elements of the future exist today – but they are just unevenly distributed . While commented in a different time, the same mindset could be applied to the presence of Artificial Intelligence (AI) today.

Though history has shown us that the pace of technology innovation routinely outstrips society’s ability to fully adapt and comprehend its implications, practical AI innovation is aggressively progressing in wealth management, including but not limited to its adoption by financial advisors (FAs). And even in this uncertain and complex world, where developing and executing a strategy can be challenging, the future is already arriving for early adopters who have made initial investments with predictive and generative capabilities.

To date, there are dozens (if not hundreds) of use cases for the practical application of AI in wealth management, and the number of use cases continues to grow, fueled by a surge in the use of generative AI in 2023, a breakout year across the industry and broader society. This progress is evident in a diverse array of applications, from client engagement and portfolio management to operational efficiency and risk compliance, marking a transformative era for the entire sector.

A Brief Review of Use Cases in Wealth Management – Illustrative

While non-exhaustive, the list of high-level use cases below references common drivers and applications of AI across wealth management – with a core focus around maximizing time spent on growing business while minimizing time and cost spent elsewhere. 

A Widening Digital Divide

Recent focus has been placed on the generation of new revenue potential and risk management, but process optimization and client acquisition are rapidly coming into focus as AI spending continues to grow.

According to the World Economic Forum, expenditure on AI is characterized into two distinct camps:

  1. High spenders who are committing more than 20% of their R&D budgets on AI, and
  2. Low spenders who are spending below that, mostly in the two to ten percent range.

High spenders are reporting significant AI-induced increases in profitability beyond the 20% critical mass investment mark, creating a digital divide in those realizing the true benefits of AI and those underinvesting.

Potential Explanations?

The relative states of data and technology infrastructure are well-known gating factors to realizing gains from AI. However, recent surveys show that there are also differing views on the value AI can bring the organization. This skepticism has probably not been helped by the poor track record of technology projects:

  • A recent Unblu survey revelated only 16 percent of stakeholders believed their technology transformations sustainably improved their performance. 
  • Forrester reported that 25 percent of technology transformations resulted in zero return on investment.

Assessing AI Return on Investment

We believe that project selection and developing a shared vision are key critical success factors for embarking on a successful AI journey. Selecting the right projects relies on choosing those which will maximize benefits and ease of implementation, to yield a higher ROI.

  1. Foundational Decisioning: First, consider whether the job title or business process in focus has enough cost or revenue attached to it to make an improvement add up.
  2. Preliminary Value Assessment: Then, assess the degree to which a job title or business process would benefit from an AI capability and the range of sophistication required in terms of process versus domain experience. Together, these decisioning criteria help us understand AI value.
  3. Tradeoff Considerations: From there, you can leverage a more traditional value versus complexity grid to prioritize the efforts against others. Complexity in AI will be where the capability must sit in terms of how much internal data and knowledge it needs to execute its tasks. From there, you can leverage a more traditional value versus complexity grid to prioritize the efforts against others. Complexity in AI will be where the capability must sit in terms of how much internal data and knowledge it needs to execute its tasks.

A Deeper Dive: Pivotal Use Cases in Wealth Management

The evolution of wealth management is poised for a substantial shift as AI becomes increasingly integrated within its practices. Financial Institutions are grappling with the challenges of heightened regulatory scrutiny, rising operational costs, and intensified expectations for personalized client services.

AI emerges as a pivotal instrument—not merely easing these pressures but also propelling the wealth management proposition into new realms of efficiency and client engagement.

Amidst a broad spectrum of potential applications, we will use the remainder of this paper on three high-cost areas of wealth management that are particularly relevant and impactful in the current environment:

  1. Nuanced management of a financial advisor's book of business
  2. Efficiency gains in client onboarding processes
  3. Advanced fortification of risk and regulatory compliance measures

Use Case 1: Managing the Book of Business for FAs: “Tending the Garden”

Managing an FA’s book of business with AI can be likened to the meticulous care and foresight required in tending a garden. AI stands in as the ultimate gardening tool, automating the mundane, labor-intensive tasks akin to weeding, providing nutrient insights to enrich the soil, and ensuring the financial growth of clients—much like the growth of plants—is robust and sustainable. By integrating AI, FAs are equipped with predictive analytics to pre-empt client needs, automated communications for regular and timely check-ins, and portfolio analytics to prune underperforming assets. Here are some areas where AI can play a meaningful role.

Predictive Analytics: Forecasting Financial Climates

Just as a gardener uses forecasts to anticipate weather changes, predictive analytics enable FAs to foresee not only market trends and client life events but also to navigate the evolving landscape of investment strategies. 

AI can guide advisors in adjusting strategies and exploring new products that might better suit the changing economic environment. Additionally, tools alert advisors to significant life milestones for clients which can necessitate shifts in financial strategy, ensuring portfolios are not only positioned for growth through all seasons of a client’s life but are also aligned with the most current investment paradigms. By leveraging AI, FAs can offer more nuanced advice, helping clients understand and adapt to a financial world.

Efficiency in Routine Operations: Cultivating Time for Growth

AI can streamline the administrative and operational aspects of wealth management, taking over tasks such as document processing, report generation, and monitoring market events with unmatched precision and speed, freeing FAs to invest their valuable time in cultivating deeper client relationships and developing more sophisticated financial strategies. Enhancing this ecosystem of efficiency, a chat bot could further change the way financial advisors manage their day-to-day activities and optimize their workflows.

Portfolio Analytics: Pruning for Prosperity 

Portfolio analytics powered by AI help FAs to prune underperforming assets—much like a gardener trims away the dead branches to encourage healthy growth. AI assists in rebalancing portfolios based on real-time market data and individual client risk profiles, ensuring a vibrant and fruitful investment landscape.

Client Financial Education: Sowing Seeds of Knowledge

AI can create customized content that addresses each client's specific financial situation, goals, and queries, derived from the intelligence gathered during advisory sessions. Nurturing clients with knowledge allows them to become more engaged and proactive in their financial journey.

Compliance Management: Guarding Against Pests

Just as a gardener must guard against pests to protect their garden, FAs must navigate a complex regulatory environment to protect their practice and clients. AI can automate the monitoring of compliance regulations, adopt processes to stay in alignment, and alert advisors to changes to help ensure that advice and marketing materials meet current standards.

Personalization and Actionable Insights: Cultivating Unique Client Journeys

Every meeting between an FA and a client holds a wealth of information. AI tools can process the nuances of these interactions, crafting personalized action plans, communication strategies, and insights that resonate with each client’s unique needs, just as a gardener tends to each plant according to its specific needs. These insights not only reflect current preferences but also enable FAs to stay a step ahead in providing relevant and timely guidance.

Use Case 2: Efficiency Gains in Client Onboarding Process

The client onboarding process is a critical first step in establishing a firm foundation for the advisor-client relationship. Traditionally, this process has been fraught with paperwork, manual data entry, and compliance checks, making it time-consuming for both clients and advisors. However, with the advent of AI, these initial stages of client interaction are undergoing a significant transformation towards efficiency and personalization.

Streamlining Documentation and Verification

The integration of AI technologies like optical character recognition (OCR) and machine learning automate the verification of client documents and data. This efficiency not only accelerates the onboarding process but also significantly reduces errors associated with manual data entry, ensuring accurate capture and storage of client information in compliance with regulatory requirements. Additionally, the combination of AI and biometric authentication methods, such as facial recognition can further speed up onboarding and add a layer of safety.

Enhancing KYC and AML Compliance

AI-driven systems excel in conducting Know Your Customer (KYC) and Anti-Money Laundering (AML) checks by efficiently sifting through vast databases to cross-reference client data against watchlists and detect potential risks.  This capability is bolstered by AI-driven risk detection models and algorithms that analyze both structured and unstructured data to unlock behavioral insights, more accurately predicting the likelihood of customers engaging in financial crimes.

Achieving Operational Efficiencies through Automation

AI contributes to operational excellence by automating routine tasks with technologies such as Robotic Process Automation (RPA) and automated document processing. This significantly reduces the time and resources required for onboarding, extending to data entry, form filling, account setup, and the extraction of relevant information from documents in various formats. Such automation not only leads to a more efficient use of resources but also supports environmental sustainability by minimizing paper-based processes.

Improving Client Communication and Collaboration

AI fosters an environment of enhanced client communication and collaboration. Systems integrated with AI enable faster and more effective interactions, ensuring clients experience a smooth and responsive entry process. This seamless communication framework is further enriched by intelligent virtual agents and bots that field initial inquiries, collect basic information, and guide clients through the onboarding steps, thereby streamlining the process and reinforcing the foundation for the advisor-client relationship.

Leveraging Predictive Analytics for Future Planning

Leveraging predictive analytics during the onboarding process offers a dual advantage. Not only does it streamline the initial setup, but it also gathers valuable data that can inform financial planning advice, predicting future financial needs or opportunities. This allows advisors to tailor their advice and suggest relevant services early in the relationship, demonstrating a deep understanding of the client’s long-term financial journey. Predictive analytics also enable a more proactive approach to improving the onboarding process by identifying potential bottlenecks and optimizing workflows for future clients.

Use Case 3: Enhancing Risk and Regulatory Compliance

In the complex and ever-evolving landscape of wealth management, maintaining adherence to risk and regulatory compliance standards is paramount. The integration of generative AI into risk and compliance functions heralds a new era of strategic, efficient, and dynamic practices. AI will enhance traditional processes, bringing automation, predictive capabilities, and strategic insights that transform risk management and regulatory compliance.

Streamlining Process Documentation with AI

Traditionally, maintaining up-to-date documentation has been a significant pain point for wealth management firms, requiring considerable manual effort and resources. AI can automate the creation and updating of process documents, including policies, procedures, and compliance manuals, in response to regulatory changes or new business practices. This automation not only ensures that documentation is current but also reduces the costs and labor associated with manual updates.

Automating Response to Regulatory Inquiries and Framework Transitions

Firms often face the colossal task of navigating through extensive risk processes and procedures, which can significantly slow down operations. The challenge of responding to regulatory inquiries and managing mandates for documenting processes and procedures presents an opportunity for AI to make a substantial impact. AI has the potential to automate the reading and responding to such inquiries, streamlining the documentation process, and ensuring compliance with regulatory standards. Moreover, as firms undergo the transition to new risk and modeling frameworks endorsed by regulators, translating all old documentation to fit new paradigms emerges as a daunting endeavor. Traditionally, this requires a considerable allocation of human resources. However, AI introduces an efficient pathway by enabling a smaller AI-oriented team to undertake this task. The use of AI not only accelerates the adaptation process but also ensures a high degree of accuracy and compliance, minimizing the risk of non-conformity.

Enhancing Compliance Monitoring and Reporting

AI enhances the automation of compliance monitoring and reporting, ensuring wealth management firms stay aligned with evolving regulations. This capability significantly lightens the manual workload, improving the accuracy and efficiency of compliance checks and reporting.

Achieving A Dynamic Risk Assessment and Predictive Compliance

Through the analysis of vast datasets, generative AI identifies emerging risks and patterns, offering a dynamic and real-time assessment of risk profiles. This allows for predictive compliance, where potential issues are anticipated and mitigated before they materialize, shifting the focus from task-oriented activities to strategic risk prevention.

Bolstering Due Diligence

The application of generative AI streamlines the due diligence process, particularly for high-net-worth individuals with complex financial backgrounds, by automating the collection and analysis of detailed information. Additionally, it bolsters financial crime prevention by enhancing the detection and reporting of suspicious activities, thus improving the effectiveness of anti-money laundering efforts.

Enabling Regulatory Change Management

One of the pivotal strengths of AI is its capacity to manage and adapt to regulatory changes proactively. Unlike traditional systems, AI can continuously monitor for changes in regulation across jurisdictions, analyzing implications in real-time and automatically updating compliance frameworks to reflect these changes. Moreover, AI can serve as a strategic advisor, offering insights into how regulatory changes might impact business operations and client relationships, thus enabling firms to adapt their strategies with agility and precision.

Looking Ahead: Partnering Together to Accelerate Your Future

As a strategic partner, we guide financial institutions through AI prioritization and implementation, with a structured framework and comprehensive services to transform vision into actionable results, positioned for immediate impact and long-term success.

Synthesizing the insights from our exploration of AI in wealth management, it's evident that AI is not merely an addition to the toolkit but a pivotal force reshaping the sector. The three highlighted use cases in this paper illustrate AI's critical role.

  1. Optimizing Financial Advisors Book of Business: AI stands as a transformative force equipping the Financial Advisors for the future, much like the diligent gardener who employs both time-tested techniques and innovative tools to ensure the flourishing of their garden. 
  2. Enhancing Client Onboarding Processes: By reimagining the client onboarding process through the lens of AI, financial institutions can achieve a balance between operational efficiency and personalized client service.
  3. Fortifying Risk and Regulatory Compliance: As regulations become more intricate and enforcement stricter, the role of AI in ensuring compliance while enabling business agility and growth will become increasingly vital.

While these areas stand out for their immediate impact, embodying the operational, cost, and risk management efficiencies that AI can deliver, they also hint at a broader transformation. This transformation transcends improving client experience, marking a new era of personalized, insightful, and secure wealth management services.

It's crucial to recognize the universal relevance of these advancements. While our discussion has centered on wealth management, the principles and applications of AI extend across the financial services spectrum. For firms willing to embrace this change, the rewards extend far beyond operational improvements, opening doors to unparalleled client service and competitive differentiation.

At Reference Point, we understand that the journey toward AI integration is unique for each organization. Our framework is designed to guide firms in identifying and prioritizing AI use cases that align with their specific objectives and challenges. For a deeper dive into this framework, see our in-depth article “Beyond the Hype: How to Create a Competitive Edge with AI.” Whether you're assessing your current state of AI readiness, exploring strategic AI opportunities, or anywhere in between in your AI transformation journey, our expertise can illuminate and help you execute on the path forward.

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    About Reference Point

    Reference Point is a strategy, management, and technology consulting firm focused on delivering impactful solutions for the financial services industry. We combine proven experience and practical experience in a unique consulting model to give clients superior quality and superior value. Our engagements are led by former industry executives, supported by top-tier consultants. We partner with our clients to assess challenges and opportunities, create practical strategies, and implement new solutions to drive measurable value for them and their organizations.

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