It’s likely that in the next few years, the wealth sector will experience dramatic change. The sector has historically been resistant to technology and change, with many practitioners very highly paid and very happy with the status quo. The upper end of the industry is characterised by strong personal relationships, high prices and a lack of transparency. But automation of processes and the adoption of artificial intelligence will lead to change sooner rather than later.
The largest institutions are looking at how they can leverage technology to improve the advisor-client relationships. For corporate buyers of technology, AI has never been more fashionable. The incorporation of machine learning into human relationships also creates ethical and regulatory challenges that must be dealt with. A question asked by the regulator is “can decisions that materially affect people’s lives be outsourced to a machine?”
In a recent survey of AI and its application to the industry it regulates, the Financial Conduct Authority found that the use of AI by regulated firms is concentrated in the back-office functions. Since the global financial crisis, managers have exercised caution in incorporating techniques that have not been thoroughly tested into the front-office, and this includes AI in risk management and portfolio creation. However, many new entrants are less wary of history and many fintechs will proudly declare that AI is dominant in front-office processes.
Clearly the financial services sector will incorporate more technology in the future and the scalability of new technologies make the emergence of new dominant platforms using AI frameworks a distinct possibility. It could in turn lead to the existence of systemically important players that may not currently be on the regulators’ radar. This could lead to systemic risk, so expect regulators globally to keep an eye on developments.
In wealth management, the growth of the industry has been hampered by the high cost of serving smaller accounts. These ‘smaller accounts’ incorporate most of society and the sector of society that could benefit the most. In industry jargon, we call this the ‘financial advice gap’. AI-led chatbots and interfaces that can replace human advisors at a fraction of the cost and interact with the mass market could soon create an expansion of the wealth management industry as technology helps to fill the gap.
Large institutions such as JP Morgan have spent billions investigating and researching the possibilities of AI and machine learning. Applications are limited not just to operational processes and digital infrastructure but could be expanded to include investment and distribution processes. Financial institutions are looking at how AI-driven processes can enable new products as well as improving existing business lines and streamlining operational processes. Good use of AI will create a competitive advantage by delivering a better product at a lower cost.
Here in the heart of the fintech ecosystem we see benefits to asset management in both the short and long term. But the benefits must be weighed against the risks, and it is not necessarily clear that the risks are understood by all participants.
The global financial crisis was exacerbated by highly evolved statistical models and an over-reliance on quantitative techniques. It is likely that this could happen again under a different badge. For statistical arbitrage, replace machine learning and we could easily again see ‘crowded trades’ and ‘groupthink’ caused by dominant strategies affecting the capital markets. As an example of hidden risks, consider that if many players began using AI strategies to allocate capital but were unable to explain in detail the mechanisms of the model due to increasing complexity, then the firm’s management and regulator would struggle to adequately monitor and supervise their actions.
A further worry is that if one form of AI were to become the dominant allocator of capital, the markets could become homogenous and brittle. As at this stage the markets would become capable of mispricing in a consistent way, leading to misallocation of capital on a large scale and adverse social outcomes. Indeed we have seen an over reliance on data and technology creating bubbles before. As the use of complicated but little understood mathematical models directly caused the global financial crisis. If for example we can remember the time that all the ‘quants’ told us that subprime was actually AAA.
The positive use cases for AI are numerous, but practitioners and regulators should be aware that new technologies create new challenges. A modular approach to AI adoption could see improved processes throughout an institution with a lower cost infrastructure in the back office, and potentially stronger CRM and risk management in the front office. But the industry should be cautious as complicated systems have created issues before and they likely will again. Fortunately this time the regulator is less likely to be ‘blinded by science’ and hopefully a slow evolution of business models with the incorporation of AI and machine learning can lead to positive change.
- This blog post expresses the views of its author(s), not the position of LSE Business Review or the London School of Economics.
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Dan Tammas-Hastings is managing director and founder of outsourced compliance and regulatory hosting firm RiskSave. After a successful career as a fixed income trader specialising in GBP derivatives at Merrill Lynch and as a hedge fund manager, managing multi-billion £ portfolios across credit and rates, he is now a specialist in risk management and is in charge of strategy and investment at RiskSave. Dan has been awarded both the CFA and FRM charters and is a graduate of LSE and the University of Cambridge.