One year ago, I gave a presentation of how technology modernisation is evolving. During that session I talked about how Gen-AI will be a major factor in meeting multiple modernisation targets, and referenced how the 2023 FIS Global Innovation Report cited that 40% of financial services firms were using generative AI in 2023, and by 2026 that was expected to rise to 89%. After the presentation I partook in a panel discussion with four senior leaders within the UK wealth industry and they were each posed the question, “where are you in your AI journey?”. Unanimously they responded, nowhere!
Surprising? Not really. Concerning? Possibly. Realistic? Likely.
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Twelve months later things certainly have moved on. 2024 has been the year of more consistent mainstream adoption of AI, yet we are still to see this manifest into anything truly core or strategic. Is this lag typical of an industry always seemingly slow to adopt new innovations or is a real-world context more relevant? When answering this question, it might be better to step back and assess why the industry is where it is, before deciding if it is lagging or showing patience.
Regulatory Risks
One of the primary reasons for the slow adoption of AI in wealth management is the stringent regulatory environment that it operates within. Wealth management firms operate under strict regulations designed to protect investors and ensure market stability. Implementing AI systems requires navigating these complex regulatory landscapes, which can be time-consuming and costly. Compliance with data privacy laws, such as the General Data Protection Regulation (GDPR) in Europe, adds another layer of complexity. Firms must ensure that AI systems handle sensitive client data securely and ethically, which often necessitates significant investment in compliance infrastructure.
High Initial Costs
The integration of AI into existing systems involves substantial upfront costs. Developing, testing, and deploying AI solutions require significant financial resources, which can be a deterrent for many firms, especially smaller ones. Additionally, integrating AI with legacy systems can be challenging. Many wealth management firms rely on established, traditional systems that are not easily compatible with modern AI technologies. This integration process can be both technically complex and expensive, further slowing down adoption.
Complexity of Financial Products and Services
Wealth management involves complex financial products and services that require nuanced understanding and judgment. AI systems, while powerful, may not yet be capable of handling the intricate and often bespoke nature of wealth management tasks. For instance, creating a comprehensive financial plan involves understanding a client’s financial goals, risk tolerance, and personal circumstances—factors that are difficult to quantify and automate. The complexity of these tasks means that AI is often used to augment rather than replace human advisers.
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By GlobalDataData Quality and Availability
Effective AI systems rely on high-quality, comprehensive data. In wealth management, data is fragmented and inconsistent, making it difficult to train AI models effectively. Ensuring data quality and consistency is a significant challenge that requires robust data management practices. Moreover, wealth management firms often deal with sensitive and proprietary data, which can limit the availability of data for AI training due to privacy and confidentiality concerns.
Talent Gaps
With almost every industry pursuing the value of advanced technologies including AI, the demand for AI talent is extremely high. Yet unlikely in days gone by, perhaps wealth management no longer holds the excitement or financial potential of other sectors. Couple this with the limitations of working within a highly regulated and mature industry and attracting talent is tough, leading to intense competition and relatively high costs for a limited pool of qualified professionals.
Slowly, slowly, catchy monkey!
Despite these challenges, the wealth management industry is gradually embracing AI. Firms are beginning to recognise the potential benefits of AI, such as improved efficiency, enhanced client experiences, and better investment outcomes. AI is being used to automate routine tasks, analyse large datasets for investment insights, and provide personalised recommendations at scale.
For example, hybrid models that combine technology and AI with a human-to-human relationship are becoming increasingly popular, offering automated, algorithm-driven financial planning services that are accessible to a broader audience while also ensuring that the qualitative aspects and trust that comes with experienced and professional individuals remain available, ensures that value can continue to be demonstrated in a way that makes sense to clients.
Russell Andrews is the Head of Wealth & Asset Management EMEA at FIS
