Generative AI in wealth management

Generative AI could add $2.6 to $4.4 trillion value across industries annually, with $200 billion to $340 billion per year in the financial sector. We take a look at how this technology can change the landscape and what kind of solutions industry leaders are working on.

Generative AI in wealth management

Generative AI is changing a wide range of industries at a pace even analysts didn't expect. One of the industries where it will have the most impact, is the financial industry. The application of Generative AI in the banking and wealth management sector could potentially add $200 billion to $340 billion per year in revenue, according to this McKinsey report.

The bigger picture: AI in finance

75% of the estimated value of Generative AI falls into 4 categories: customer operations, marketing and sales, software development, and R&D. In the banking industry, especially risk & legal and software engineering will be impacted, thanks to Generative AIs ability to process large amounts of legal texts rapidly and increase the speed of software development.

Generative AI can today already improve the efficiency of lower-value but time-consuming tasks, such as required reporting, monitoring regulatory developments, and collecting data. McKinsey calculated that productivity would increase 2.8 to 4.7% annually, or an additional $200 to $340 billion in annual revenue. And it will happen very soon: PwC estimates that the adoption of artificial intelligence for different use cases will happen in less than 7 years.

Graphs showing the potential AI consumption impact, the amount of possible personalization, data availability, and estimated adoption time frame
The financial industry will together with healthcare and automotive be most impacted by Generative AI, with full adoption of the technology in less than 7 years (Source: PwC)

One of the current limitations of AI in the financial industry is a lack of trust by consumers. However, the accuracy and security of the models are rapidly improving. In a consumer study from Additiv, already more than 60% of people between 18 and 44 years old would consider switching to a different bank if it's perceived as more innovative.

Bar chart showing the percentages of generations willing to move banks if they offered more innovative services
Innovation is a competitive advantage for banks, especially among younger generations (Source: additiv)

Immediate impact with AI APIs

If your company isn’t running experiments with Generative AI, you’re already falling behind. - additiv

The Proof-of-Concept (PoC) process is a relatively quick approach to apply Generative AI in your business. In this approach, APIs available from platforms such as OpenAI's ChatGPT or similar large language models (LLMs) from other vendors are leveraged for your business use cases. These models are already trained on public data, allowing to be put faster to use. On top of that, these open-source LLMs are a lower-cost alternative to the leading closed-source LLM providers, and better privacy as well. These open-source Generative AI APIs can give your workflows a boost forward and capitalize cost-efficient on the low-hanging fruits, such as data analysis, knowledge management, and content generation.

Long-term approach with fine-tuned models

In the long run, a custom system fine-tuned on your own proprietary data is the best option. Instead of working from an open-source model pre-trained with public data, the AI engineers fine-tune the LLM themselves. By fine-tuning the model with the unique dataset, they can create models that are specifically tailored to your industry, domain, and client needs. The end result is a more secure and personalized system with the following benefits:

  • Customized recommendations and financial plans: Fine-tuned LLMs enable highly personalized investment recommendations and financial advice based on clients' unique needs, short-term and long-term goals, preferences, and risk tolerance. These analyses can be repeated every time the client changes their goals.
  • Predictive insights and improved decision-making: Leveraging proprietary data with LLMs provides predictive insights into market conditions, market trends, competitor strategies, and client behavior, empowering wealth management firms to anticipate shifts and develop proactive strategies for success.
  • Streamlined operations: LLMs automate routine tasks like data analysis, report generation, and compliance checks, freeing up time to focus on strategic aspects of client portfolios. Internal knowledge management systems can reduce time spent looking for internal information and free up valuable work time.
  • Risk management: LLMs fine-tuned with historical financial data can predict and assess investment risks, facilitating proactive risk management practices and protecting clients' investments.
  • Competitive analysis: Fine-tuned LLMs can analyze market trends, competitor strategies, and industry developments. These insights enable wealth management businesses to identify opportunities, stay ahead of the competition, and refine services to meet market demands.
  • Regulatory compliance: Proprietary data handling is improved by training LLMs to ensure compliance with regulations like GDPR and financial data protection, safeguarding sensitive client information and maintaining trust.

Several firms have already been experimenting with Generative AI for a while and have been creating tools for their clients and/or their advisors. Charles Schwab has made a thematic stock generator to assist with identifying trends and picking stocks. And Savvy Wealth recently introduced a Generative AI-powered platform for advisors with high net worth clients, specifically built for their team. It streamlines several processes such as new account onboarding, client portfolio recommendations, financial planning, and personalized communications across marketing channels.

Industry's race

Industry leaders are recognizing that Generative AI will have a significant impact on the wealth industry. It comes as no surprise that in 2022, 80% of the CFOs surveyed by Gartner expected to spend more on AI in the next 2 years. Some experts even call this development "integral to the future of the industry", and see a wide range of potential applications of the technology.

The examples are numerous, also among the most well-known names. For example, Morgan Stanley is testing and rolling out an advanced internal chatbot for its 16,000 financial advisors, powered by OpenAI. This system allows them to easily access, process, and synthesize the bank's huge data and knowledge resources. Earlier, Deutsche Bank announced a partnership with NVIDIA to embed AI and ML in their financial services, by developing applications for risk management and customer service. JPMorgan Chase on the other hand is developing a financial tool for its customers to select their investments, combining cloud software with generative artificial intelligence. Goldman Sachs looks more at the software development side of their business and is internally testing generative AI tools to assist developers with writing code.

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Generative AI holds immense potential for bankers and wealth managers to revolutionize advisory services, offering efficient and personalized solutions. By harnessing AI-powered tools, your firm can optimize workflows, save time, enhance efficiency, and cut costs, gaining a competitive edge in the ever-evolving financial landscape.

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