Privé Conversation: AI - but why? [Vol. 1]

7 minute

Narrative is such an important component of financial markets and their peripheral industries. This year’s story, in case you missed it, is about the breakthrough of Alternative Intelligence (AI) into the mainstream. Of all the definitions, this simple one from Gartner summarises it well:

“Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions”.

Earlier this year, we were told to expect AI as the next ‘new, new thing’ - transpiring in a genuine mania around OpenAI’s generative tool ChatGPT. Will it change everything? And why now?

Results of a Privé poll - February 2023 (source: LinkedIn)

What do you believe will be the biggest trend in Wealth Management this 2023?

- Increased adoption of AI 45%

- The growth of the Super-App 18%

- ESG investments go mainstream 18%

- Alt investments for retail 18%

As with any technology, an AI solution can only be successful if it fixes an identifiable issue, and leads to an improvement in net outcomes. In hype cycles, solutions may well appear to problems that don’t yet - or may never - exist. We should be circumspect about which AI solutions can add value and the risks of using them. Where complex ecosystems interlock and interact and fiduciary duty is paramount, solutions should be additive and to the end customer’s benefit, while causing limited disruption.  Caveat implementor.

For disclosure: none of the writing of this article, or the underlying research owes its existence to any AI processes (apart from an internet search or two). We can’t guarantee this will always be the case. Financial services relies on extensive one-way, written communication, which must be timely and factually correct. As markets continue to speed up, more product complexity requires constant commentary, investment research, risk disclosure, regulatory and client reporting. How to keep up?

Morgan Stanley Wealth Management announced in March that it would make a generative AI tool available to its financial adviser community to synthesise relevant content for customers. For those overwhelmed by the daily flow of information delivered into mailboxes and apps, this is a clear step forward - and at a macro level, may even help bridge the talent and advice gap. Automated language tools are in fact already being applied in many different ways across client communication, including advanced analytics and the use of chatbots, enabling a two-way dialogue in the process.

So, should we pity the copywriter, the client services officer and the sell-side research analyst? On the contrary, if used correctly, these assistive technologies can make them better informed and more productive. Meanwhile, there is still plenty of work to do to ensure these services are rolled out in a robust environment where both the inputs and outputs are strictly controlled. That requires extensive human intervention.

Alternative asset managers have long used machine learning, a form of AI, for idea generation, portfolio construction and trading inputs, to find patterns in big, alternative data sets. Such tools are often used with a high degree of autonomy. On the flip side, these proprietary investment processes are difficult to scale, hedge funds not being known for their client-centricity, or transparency. How then to deliver these to a broader investing public?

Traditional asset managers and those with retail clients have been seemingly slower to integrate AI into their investment processes, according to a 2019 CFA report. However, the ultra-fast news cycle (contagion from this year’s US bank failures a case in point), could make traditional research obsolete in many cases. Extending the use of AI into portfolio construction* and risk management, there is scope for asset managers to adapt their products to end customers’ needs and preferences much more creatively and accurately.

Therefore, across financial services, AI and machine learning are already making significant inroads. Wherever large datasets are to be analysed to support decision-making or identify patterns, AI can underpin productivity gains and bring operational efficiency. In rules-based environments, AI may crucially assist with compliance, notably when associated with other technologies such as distributed ledgers. Finally, as service providers increasingly get to grips with sustainability, AI solutions may be key to fixing the data issue. More quantifiable and comparable ESG data can be produced, personalised to each adviser’s discrete mix of materiality, impact or philanthropic priorities.

Nonetheless, having identified issues that can be solved by an AI solution, financial sector actors must still be able to explain, with conviction, the intellectual basis for portfolio decisions or advisory propositions. Careful planning, execution and adequate staffing to maintain AI projects will still be required. Given the complexity of the ecosystems in which our industry operates, the interpretation and interaction between different AI solutions may still have to be assessed by plain old ‘I’; cross-functional thinking in the organisation is a must.

In conclusion, the buzz around AI in financial services is due to its ability to assist in productivity and efficiency gains, client engagement and compliance. It is also partially a natural extension of alternative investment techniques into the retail space and a bid for better-managed portfolios. As information overload increases exponentially and decision-making struggles to keep time with markets, AI can lead to more personalised service, scale and reduced cost. However, to achieve this, there may need to be a change in culture, even business models in financial services firms. Incumbents may need to learn to be less proprietary and more open to collaboration across firm and functional lines. Given the uncertain market backdrop, with severe headwinds for the business of gathering and managing customer assets, it’s possible that for the industry, more than the narrative is in need of change.

*Privé Technologies offers personalised portfolio optimisation solutions based on a genetic AI methodology (AI GO).

For further details of Privé Technologies award-winning solutions for asset, wealth managers and insurance companies, please visit and our dedicated LinkedIn page.

Sales Contacts:

Rutul Gandhi

Chief Revenue Officer, APAC

Björn Torkar

Chief Revenue Officer, EMEA

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