With the increasing volatility of markets and complexity of products available for investors today, wealth managers are bound to use artificial intelligence (AI) as part of their overall strategy. There is a need to embark on new technologies in order to keep up with the ever-growing challenges faced by wealth managers and their clients alike. These challenges are showing to tap capital preservation and portfolio optimisation, and feature heavily in conversations between wealth managers and their clients.
Wealth management is complex, and when AI features in the dialogue, a number of questions come up: will it help the market quickly solve pain points? Will jobs disappear? Fintech applications and wealth management businesses today still have a long way to go in terms of achieving the ideal practices presented by AI and finance thought leaders.
There are several ways in which we can apply AI strategies in order to solve the main challenges faced by wealth managers today.
Compliance risk mitigation
A 2017 Deloitte survey found regulatory compliance risk as among the top three risks faced by investment management firms that would increase in importance over the next few years. It also suggested that a regulatory-ready organisation has three attributes: a framework for risk assessment, a mechanism to track and measure risk, and a method to allocate resources based on its understanding and experience of risks.
A wealth management firm would always be subjected to regulatory mandates and depending on the institution’s size and reach – whether local or global – the capability to manage compliance risk can be eased out with the use of AI. The easiest way to do this is to implement the technology in line with the institution’s already-available framework for regulatory compliance. Leveraging AI in risk frameworks is an sensible approach, because the fundamentals of the technology define its use case as having a level of freedom that does not have predefined sets of underlying factors to achieve certain outcomes. Implementing AI based on risk frameworks enables institutions to assess the intelligence taken from information that is readily available to them. Not considering risk frameworks when applying AI could pose threats to an institution’s compliance.
Client suitability is an area where AI can be used to protect both the client and the institution in terms of fairness and limiting risks for the institution. Most wealth managers today are intimidated by AI’s many possibilities. Providing advice based on client suitability is a good start because it will consider data that is easily obtained and would tackle processes already in place while mitigating risk.
Another area that AI can be applied is counterparty risk, in terms of the institution’s product providers for its complex financial instruments. According to EY, AI-based analytical platforms can manage risk by integrating a host of different information sources about suppliers – from their geographical and geopolitical environments through to their financial risk, sustainability and corporate social responsibility scores. Relationship managers will be able to trade recommended investments to client portfolios with a single click and at the same time be able to see instant updates on profiles of newly adjusted portfolios. Intuitive tools allow real-time compliance checks, ensuring every trade meets compliance standards. AI-based applications allow client advice to be based on individual trades, so wealth managers are covered at every angle. Relationship managers will be more proactive in providing the most suitable investment strategy and asset allocation for their clients.
After taking care of risk and compliance, the more exciting part comes to play in the form of portfolio optimisation. More than safekeeping capital investments, high-net-worth individuals expect high investment returns from their wealth managers. Aligning risk frameworks with investment strategies that can be done with AI will maximise efficiencies of an institution’s fund administration processes. Within seconds, wealth managers can access the overall positions as well as the objectives, investment style and strategies of every fund available. Accurate NAV calculations is an essential part of an institution’s fund administration process. AI-based applications automate NAV and pricing calculations, which can also be applied across an institution’s range of funds, including more complex funds like multi-class and master-feeder funds, while also having variation checks and pricing controls.
Once the full history of a fund’s activity, accruals, fees and NAV is considered, wealth managers are more capable of adding other factors into play in order to achieve optimum return on investments in a client’s portfolio. Risk metrics such as volatility, VaR and expected shortfall tend to quite abstract for the average investor and therefore not enough for portfolio optimisation. With AI, assumptions can be done based on quantitative finance calculations on concrete economic factors that clients and wealth managers evaluate. AI-based applications can help institutions add market and credit risk factors to investment strategies where advanced models come into play. When used for portfolio optimisation, AI-based applications seek to minimise risk metrics that use advanced models such as volatility, tracking error, parametrical and historical expected shortfall, Monte Carlo expected shortfall and maximum drawdown. Wealth managers will be able to visualise graphically a portfolio’s components, benchmarks and credit risks, as well as how those credit risks are being reduced. Investment strategies can also be designed to minimize brokerage fees so that the client achieves a higher alpha. Portfolios can be consolidated and linked to a model, empowering wealth managers to fine-tune their investment strategy to make value-creating investments. AI allows wealth managers to compute and analyse performance using a variety of methods against individual or blended benchmarks.
Hedging against market volatility risk
High-net-worth individuals expect good returns on their portfolios.
Identifying metrics that can be easily tracked by AI is the key to setting a wealth manager’s approach to capital safekeeping. Focusing on risk mitigation is a good building block for capital safekeeping because the direction of the markets and its volatility is unknown, while product and client information is not.
The ability of AI-based applications to efficiently use data for forecasting can be useful for hedging against market volatility. Clients expect wealth managers to be up to date with market trends and that the advice recommended for their portfolios already considers market factors. AI technology allows institutions to use complex modeling for the purpose of seeing how the returns will fare against those of the market. The risk of market factors to a client’s portfolio is also considered and AI will be able to aid wealth managers in explaining the impact of these and other sources or risk – such as economic factors and idiosyncratic risk. Wealth managers will gain a more accurate prediction for the performance of their client’s portfolio by incorporating economic forecasts and identifying relevant seasonal trends to better understand the movements of their client’s assets.
The volatility of markets today is forcing investors to stay within the comforts of capital preservation. It is an ongoing issue across the finance industry. Implementing AI-based applications allows wealth managers to measure volatility in a timelier manner, by looking at the economic cycles of the assets in their client’s portfolio. Using these factors, wealth managers can easily explain to clients the changes in volatility occurring at any one time.
Wealth managers often talk about the importance of having a complete 360-degree view and using this information to give their clients an optimum customer experience journey. Adding AI into play will amplify what wealth managers mean when they talk about having a that view. As soon as their conversation begins with a client, the view is in place, with insights in where their client’s portfolios have been and where they are headed to. That will give the wealth manager the confidence to make suggestions about where they believe they can make improvements.