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Five priorities for winning with GenAI in wealth and asset management

Wealth and asset managers have the opportunity to reimagine their business models and transform their operations with GenAI.


In brief

  • The GenAI era has arrived in wealth and asset management – with the majority of firms actively investing, making plans and building teams. 
  • Among many potential use cases, firms are prioritizing those with the highest near-term impact on the value chain as they navigate barriers to adoption.
  • Strong governance models and controls are needed to address a range of tech risks, including data privacy and accuracy, model bias and hallucinations.

The potentially transformative impact of artificial intelligence (AI) and generative AI (GenAI) has caused it to move rapidly from adoption by first movers only to much broader uptake in wealth and asset management. The majority of firms are investing or planning to invest in GenAI use cases and capabilities, according to recent research from EY-Parthenon.

Our study of more than 225 senior decision-makers revealed that three in four firms (75%) are mobilizing GenAI teams.

GenAI investments
84%
of wealth and asset managers are already investing in GenAI or making plans to invest.
GenAI teams
75%
of wealth and asset managers are already building or mobilizing GenAI teams.

Across the industry, the primary drivers of GenAI investments are:

  • Client experience enhancements (cited by 69% of respondents)
  • Task automation (62%)
  • Cost reduction (56%)

Client onboarding and marketing and client acquisition are areas where wealth managers expect to see the greatest time and cost savings in the front office. Asset managers cite onboarding and portfolio management as the two areas offering the greatest time and cost savings in the front office.


The research makes it clear that leaders see GenAI as a means to address areas of the business that have long needed improvement. For instance, the automation of manual processes and administrative tasks that will free resources to focus on higher value activities. Deployments won’t necessarily be easy, however, or the scaling smooth. Key findings from the research highlight common barriers to adoption and five priorities for long-term success.

Download: Generative AI in wealth and asset management


1

Chapter 1

Common barriers to GenAI adoption in wealth and asset management

Business leaders need to plan for and overcome four barriers to adoption identified by research.

Data concerns – accuracy, reliability, privacy and security

A clear majority (63%) of respondents are concerned about external data in GenAI use cases. These leaders cited data accuracy (67%), data quality (45%) and data privacy (42%) as their top concerns. Addressing these issues is critical especially because external data can maximize the usefulness of large language models (LLMs) and client-facing applications.
 

Regulatory compliance, governance and ethics


Though they are still evolving, regulatory expectations extend beyond legacy issues, such as data privacy and cybersecurity. They also include ethical standards that ensure that algorithms are unbiased and that AI decisions and outputs are fair and transparent. (Learn more about EY’s perspective on AI policy and regulation.) While guidelines are evolving in some jurisdictions, regulators everywhere – as well as other stakeholders, including customers – expect firms to promote responsible and ethical use of GenAI.

Though regulators in some jurisdictions have issued regulatory standards, many leaders perceive a lack of clarity around guidelines for GenAI usage. This perceived regulatory ambiguity surrounding GenAI may slow adoption, according to a majority of survey respondents. The concern is highest among private banks (65%) and wealth managers (62%) and somewhat lower among asset managers (58%).

Barriers to GenAI investments
62%
of respondents are concerned about perceived regulatory ambiguity surrounding GenAI.

Potential overconfidence regarding internal capabilities, including technology and talent

Strong internal capabilities, including technology infrastructure, talent and controls – are necessary for success with GenAI. More than two-thirds of respondents say that their firms have what they need to implement GenAI use cases.

Internal capabilities
68%
of respondents say that their firms are well-equipped in terms of infrastructure, internal controls and internal talent to implement GenAI use cases.

Executives that are tasked with leading AI implementations may want to guard against overconfidence about internal capabilities, however, given the prevalence of legacy systems and the shortage of AI talent across the industry. In fact, when asked to identify barriers to establishing a dedicated GenAI team, insufficient internal expertise was the top challenge, cited by 63% of respondents. Developing internal AI expertise via training and upskilling programs will be key to both deploying effective tools and promoting employee adoption.

The survey respondents also recognize the need for external partnership and ecosystems to extend their own capabilities: 66% of wealth managers and private banks and 70% of asset managers said that they are considering partnerships to execute on GenAI priorities in the front office.

Unclear business case

While wealth and asset managers see many potential applications for GenAI, 44% of respondents said that a lack of clarity around actual impact on the business is a concern for adoption. A strong and quantifiable business case for individual use cases, with clear ROI targets, will help firms prioritize their investments. Business cases should quantify the benefits of GenAI applications for different parts of the business, factor in development and implementation costs, and specify how GenAI deployments align to strategic business priorities.

2

Chapter 2

Five priorities to advance the AI journey

Focus on these priorities to realize the value for GenAI adoption and create long-term value.

The majority of survey respondents see the massive potential upside of GenAI: 86% of wealth and asset managers believe that it will increase productivity and 66% anticipate an enhanced client experience. Further, 68% of wealth managers expect the greatest time and cost savings from GenAI to come from improved customer servicing.

 

So, the question is: how can firms realize the value? The following priorities can help all types of firms chart a course to long-term value.


It’s important to remember that the most effective use cases will have humans at the center. Advisors would always review and check GenAI-product investment recommendations for wealth management clients, for instance. And in asset management, for example, analysts would validate that investment guidelines coded by GenAI tools are correct. Such an approach can generate value, raise the workforce’s AI IQ and mitigate risks.

1. Reimagine the business model

With transformative technologies, firms can set themselves up for the greatest success by developing a strategic vision for the disruption of their business models and aligning their efforts and investments accordingly. Wealth managers should think about how GenAI can democratize advice and transform client and advisor experiences. Asset managers can search for alpha by taking advantage of external information to generate investment ideas and differentiate their offerings.

Where to act now

In creating a vision for the future and a roadmap for the next three to five years, firms should apply lessons from previous implementation of high-impact technologies. Firms should reimagine legacy areas that are ripe for overhauling and optimization while balancing broader enterprise risk. Use initial use cases as a testing ground to build up the skills and insights necessary to launch into more strategic business shifts. Wealth managers should focus on knowledge management, client contact centers and other areas where automation can free up advisor time. Asset managers should focus on automating manual processes like client onboarding, lead generation and prospecting, and augmenting investment strategy and portfolio development. 

Looking ahead

The future-state vision should be continuously refined based on results from initial deployments, advancements in LLMs and regulatory developments. Asset managers should enrich portfolio strategies in real time through continuous assessment and validation of market data via analyst reports, transcripts from quarterly earnings calls and other data, as well as explore further product customization. Wealth managers should look to personalize client interactions, advice and optimize portfolio construction through the usage of GenAI and external data.

Over the longer term, both wealth and asset managers will use GenAI to attract and capture new business. For asset managers, GenAI may enable new product development, as well as direct distribution and entry into new channels. For wealth managers, GenAI tools, can inform and empower client-facing teams to identify and connect with prospects and ultimately offer hyper-personalized solutions.

2. Rethink operations

Automating and optimizing repetitive, data-driven tasks can yield significant gains across the business. Data aggregation and analysis and data entry are promising starting points for many firms. Tracking results with appropriate metrics can help firms identify the most beneficial use cases and adjust future investment priorities.  

Where to act now

Wealth managers can use GenAI to build new support models for advisors and smarter processes throughout the business, including those that help advisors accelerate positive client outcomes, including report generation, account setup and real-time risk monitoring. Asset managers can use GenAI to enhance functions like trade processing, performance management, fund accounting and administration. They can also automate operational tasks, like tracking and coding of investment management agreements and other compliance activities. Cash management is another area where asset managers can adopt GenAI to optimize performance.

Looking ahead

Firms can deploy GenAI tools to monitor market volatility and produce appropriate client guidance, track shifts in the regulatory and legal environment, and monitor firmwide compliance with investment mandates and guidelines. 

3. Build a robust governance framework

As GenAI models are adopted more broadly across the front, middle and back offices, they will heighten existing risks and introduce new ones. This makes strong risk management governance more important than ever, for both internal applications and third-party tools. Clear policies, strong testing practices and oversight of people are all necessary to address new and heightened risks (e.g., bias, accuracy/hallucinations, changes introduced with new model releases). They also form the heart of effective and resilient risk management strategies.

Where to act now

Testing capabilities should be expanded and enhanced, with procedures incorporating advanced analytics and agile methods (e.g., scenario simulations) to identify and adapt to emerging risks and insights.

Roles and responsibilities for GenAI risk oversight should be clearly defined, from the board to the front lines of the business. Organizational policies should guide employee usage of GenAI (e.g., prohibiting the use of proprietary data with external tools and models). Broad-based controls for GenAI development, monitoring and risk management should be applied consistently across use cases. Upskilling for risk and compliance teams should be incorporated as part of a broader redesign of governance protocols.

Asset managers that use GenAI to automate compliance and investment guidelines should keep humans in the loop as final reviewers. Wealth managers should put mechanisms in place to monitor conversations between their clients and chatbots.

Looking ahead

Firms should adjust governance models based on technology advancement and regulatory changes, applying enhanced controls and advanced testing techniques to both existing applications and development of new tools.

4. Invest in key data, talent, and infrastructure capabilities

Long-term success with GenAI requires strong capabilities in a wide range of areas, from data management to talent and technology infrastructure.

Where to act now

Strong data management capabilities can provide the foundation for effective security protocols and streamline development of GenAI applications. Robust data capabilities and partnerships can accelerate firms’ ability to mine and monetize internal data assets. In addition, modernizing technology infrastructure, including cloud computing, data lakes and application programming interfaces (APIs), will be necessary to streamline and secure data access for partners.

Looking ahead

Leaders will need to determine the capabilities and infrastructure components that are most important to retain in-house for the future. Build knowledge graphs of internal expertise and consider shared services or centers of excellence for deploying scarce talent. As new capabilities mature, update investment priorities and continue to explore sourcing options for key skills, technologies and services.

5. Build partnerships to develop ecosystems

Partnerships can help firms bridge their GenAI tech and talent gaps, accelerate innovation and gain long-term competitive advantage. Firms should consider partners that can help them execute on high-priority use cases across the front, middle and back offices. Business process outsourcing firms may be a viable option for automating customer service tasks.

Looking beyond transactional outsourcing and software-as-a-service relationships, full ecosystems and strategic partnerships with technology or other firms can promote assistance in LLM access, application development and other critical areas.

Where to act now

Potential partners should be assessed based on their ability to accelerate initial use case development and provide access to scarce skills, advanced tech and high-value data. Security practices and cultural fit are other important criteria. Ecosystem strategies should align directly to top business objectives and prioritize participants with mature capabilities in targeted areas.

Looking ahead

Future partnerships and ecosystems should be designed to accelerate innovation, new product development and operational excellence. Continuous monitoring of results based on clear metrics will help determine which partnerships should be consolidated and expanded.

Summary

To make the most of their GenAI investments, wealth and asset managers will need to carefully assess and prioritize areas of opportunity in line with their broader business objectives, reimagine the art of the possible and define a roadmap for future innovations, build the necessary internal capabilities and establish sufficiently robust governance frameworks. While the effort will be considerable, the transformative potential of GenAI more than justifies it.

Riya Sen, Principal, EY-Parthenon, Ernst & Young LLP and Martin Rogulja, Senior Manager, EY-Parthenon, Ernst & Young LLP were contributing authors for this article.


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