How AI could change wealth management

These three impacts alone have the potential to transform the industry.

The most straightforward impact to understand is how AI enhances efficiency. By automating repetitive tasks such as data processing, know-your-customer due diligence, and compliance checks, AI tools free wealth managers to focus on strategic decision-making and client engagement. For instance, Google's Vertex AI platform can generate comprehensive reports in natural language, while Microsoft's machine learning (ML) technology helps identify risks, spot trends, and investigate inefficiencies.

In equity and credit research, natural language processing (NLP) has traditionally been used by a privileged few. However, with the availability of powerful computing resources for rent from cloud service providers (CSPs) such as AWS, Azure and GCP, access to NLP has been democratised¹. The Bloomberg terminal, for example, uses NLP to summarise conference calls, extract key themes from earnings seasons, and create custom curves for bonds and indices, thereby generating insights and alpha, and saving analysts and fund managers a lot of time.

By suggesting real-time portfolio adjustments and dynamic risk management, AI tools can optimise operations and potentially improve risk-adjusted returns. There are low-code and pre-code apps that can automate business insights and can integrate real-time data from over a thousand apps and finance tools. These tools free up wealth managers to engage in higher-value activities such as offering more tailored investment solutions for clients and more client engagement.

These tools free up wealth managers to engage in higher-value activities such as offering more tailored investment solutions for clients and more client engagement.

Making decisions in the age of Big Data can be challenging. How does one distinguish noise from signal? Often it takes a lot of time, skill and experience to wring out actionable insights from a tsunami of data. AI tools perform sophisticated analyses of financial data, enabling better asset allocation and risk detection. These tools can analyse market trends and generate insights, helping wealth managers optimise portfolios and automate some trading processes.

Personalisation of client solutions remains the gold standard in client service. AI can turbocharge that goal and enhance client interactions with more relevant and timely data. AI enables personalised investment strategies by analysing vast datasets to tailor financial products to individual client profiles. This personalisation enhances client satisfaction and loyalty and has been a source of enduring competitive advantage.²

There are also environmental, social, and governance (ESG) benefits from using AI. One of the major hurdles for investment is poor and inconsistent data quality. Machine learning can be used to assess reams of structured and unstructured, and quantitative and qualitative data relating to ESG could provide deeper and more actionable insights. Further, this can be done in real-time and corrective actions taken earlier.

One of the most cited social risks is the loss of jobs. There is little doubt that wealth managers, analysts, IT and even the executive management teams will need to upskill. This means going beyond theory to practical skills – using dashboards daily, adjusting the technology, or even overriding its decisions. Scott Galloway, Professor of Marketing at the New York University Stern School of Business, put it well, “If you are an investment manager, you are not going to be replaced by AI. You're going to be replaced by somebody who knows how to use AI better than you do.”

Talent war is a more pertinent worry in the near term than loss of jobs. For an effective and robust AI system, the schematic below shows some jobs that are required. The number of people with these skills is still small and demand is rising exponentially.³

Source: Chartered Institute of Securities and Investments
 

So what should executives know? First, they will need to invest heavily in AI infrastructure, talent and upskilling the current workforce – everyone needs to know what and how to use AI tools, and perhaps more importantly, when not to use the tools. Secondly, cybersecurity and data security will become ever more important. Data management systems need to be secure and robust.  People and data will be key strategic assets. Finally, speed is of the essence. A chasm will open up between the leaders and laggards. Those firms that dither will be disrupted.⁴

Source: Chartered Institute of Securities and Investments
 

Despite its benefits, AI poses challenges such as data integrity issues and the potential for incorrect decisions based on flawed data patterns. Human oversight remains crucial to ensure AI-driven strategies align with client needs and ethical standards. The industry continues to invest significantly in AI infrastructure, expertise, training, governance and software architectures to provide robust AI systems that best serve all stakeholders and do no harm to humans.

AI has proved thus far to be no hype. If anything, the genie is out of the bottle and those who do not adopt and adapt will be left behind. The integration of AI is expected to grow, with the global market for AI in asset management projected to reach $17.01 billion by 2030.⁵ As AI technologies mature, upskilling in AI becomes essential for professionals in the industry to remain competitive.⁶

While AI can automate many tasks, humans are still needed to interpret the insights, make decisions, and ensure the ethical use of the technology. Human intelligence has a key role to play in investment management and distribution workflows. Management needs to set guardrails on both the data and models.

¹ https://www.nvidia.com/en-gb/data-center/gpu-cloud-computing/
² https://www2.deloitte.com/content/dam/insights/articles/glob187719_dim33/Deloitte-Insights-Magazine-Issue-33.pdf
³ https://www.otago.ac.nz/__data/assets/pdf_file/0012/312060/https-wwwotagoacnz-caipp-otago828396pdf-828396.pdf
⁴ https://assets.kpmg.com/content/dam/kpmg/uk/pdf/2023/11/cee-uk-report-2023.pdf
⁵ https://www.pershing.com/content/pershing/us/en/insights/the-impact-of-ai-in-wealth-management.html
⁶ https://www.hubbis.com/article/the-future-of-ai-in-wealth-management-perspectives-from-a-post-event-survey-on-september-19