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AI’s strengthening grip on asset management
The speed at which artificial intelligence (AI) is developing is now one of the hottest topics in the mainstream media. Less well-covered is how AI has already tightened its grip on asset management. Asset managers, hedge funds and family offices are using AI across their businesses, and the technology’s influence is growing rapidly.
Terminator fears dominate the headlines while AI tornado sweeps business
The blistering pace at which AI is advancing has truly captured the media’s attention in 2023. Many reports are sensationalist, focusing on the existential threat AI poses to humans. Others, however, highlight the remarkable inroads AI is making into the business sector.
In the space of a few months, for example, AI has transformed the way the UK energy firm Octopus interacts with its clients. AI is now replying to customer emails, doing the work of around 250 people. Moreover, the emails deliver 80% customer satisfaction — significantly higher than the 65% achieved by skilled, trained people.1
How AI is being deployed by investment managers
AI is also advancing rapidly in the financial-services sector. Bloomberg reports that ‘the AI revolution is unfolding on Wall Street as wider interest grows in the evolving technology and its likely impact on business’. The news agency cites the examples of Deutsche Bank (which is using AI to scan wealthy client portfolios) and ING (which is screening for potential defaulters). Meanwhile, JP Morgan & Chase is ‘hoovering up talent, advertising for more AI roles than any of its rivals’.2
However, that is likely to prove the tip of the proverbial iceberg. Below, we outline the four main ways in which the investment world could be transformed by AI.
Customer services among the main benefits of AI
AI’s ability to generate alpha in challenging market conditions – by analysing vast quantities of data and providing predictive insights, portfolio optimisation and risk-management tools – explains its growing popularity with fund managers. In addition, AI techniques can reduce transaction costs by automatically analysing the market and subsequently identifying the best time, size and venue for trades.
Enhancing operational efficiency
The disruption of 2022 had a stark impact on asset managers’ financial performance, according to the consultants EY.3 Falling markets and net outflows caused assets under management (AUM) to fall steeply, driving down revenues, while inflation pushed up expenses. Little wonder, then, that asset managers are keen to embrace the cost-efficiencies promised by AI. The technology can be applied to repetitive processes, increasing both accuracy and operational efficiency. AI can also be used to identify whether a customer might be about to move to a competitor, enabling it to act pre-emptively to recommend products and services that will retain the customer’s business.
Improving content distribution
AI has made a significant impact on content distribution, revolutionising the way information is shared and consumed online. It helps advisors generate more insights, customise content more effectively, and deliver it to clients with greater agility and speed.
However, there are limits to the use of AI in generating content because of the risk of disseminating false or simply misleading information. Researchers have found, for example, that AI systems such as ChatGPT may be using concepts that humans do not understand. That means there is little way of knowing what biases might be involved in the system, or if it is providing false information to the people using it, since there is no way of knowing how it came to the conclusions it did.
AI is a game changer for risk management, according to the consultancy Deloitte.4 Machine learning models, for example, can assess the creditworthiness of borrowers, predict potential defaults, and evaluate the overall risk exposure of a portfolio. These abilities enable financial institutions to make more informed decisions and allocate resources more effectively.
The disadvantages of AI
The drawbacks include the fact that AI models are often complex and opaque, making them difficult for managers to monitor and scrutinise. The models’ reliance on – and sensitivity to – data can introduce a considerable source of risk. AI models can, for example, be improperly trained as a result of using poor-quality or insufficient data.
Ineffective human supervision, meanwhile, might lead to systematic crashes, an inability to identify inference errors, and a lack of understanding of investment practices and performance attribution by investors. Lastly, there are considerable development and implementation costs, and it’s unclear as yet whether this investment will pay off.
Conclusion: balancing technology and talent the key to managing the transformation
According to Deloitte, implementing AI across these areas could rapidly transform business models, operations and internal capabilities. The consultancy added, however, that to fully benefit from AI, firms will need to carefully consider and manage the intersection between technology and talent.
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