How to bring wealth management into the 21st century
Much of today’s wealth-management industry is predicated on a myth: that portfolios are being optimally managed based on timely information and sophisticated analysis. The reality is different.
It’s high time wealth managers mastered smart data analysis to assess the ever-changing nature of risk. Only then will they be able to build well-balanced and bespoke client portfolios that benefit from transparency and diversification
If banking has been slow to apply data analysis and automation to its daily operations, then the pace of innovation in wealth management has been glacial. According to a recent study by KPMG, only 27 per cent of wealth-management firms use data analysis, and just over 30 per cent have moved to automating transactions. Their financial modelling is simplistic, often relying on week-old or even month-old information and bad data quality.
This represents a terrible lost opportunity, because smart data analysis and automation can accurately quantify risk and enable wealth managers to build and maintain well-diversified portfolios with the client’s risk appetite and objectives in mind.
Data analysis and automation have already revolutionised retail and the entertainment industries. They have the potential to do the same for banking, agriculture, construction, transport and manufacturing, making them more efficient and more customer-focused. Yet these innovations have yet to make their mark on wealth management. Why?
Some reasons are obvious: wealth management is a conservative industry that is built on the trust between wealth manager and client. These clients often belong to an older, less tech-savvy generation. Their appetite for risk is very low and they are averse to change.
But it is equally true that the industry lacks the skills required to implement change. And there is more.
Until recently, we simply did not have the computing power to crunch the vast number of variables that must be examined when building and maintaining a portfolio. For Amazon, data analysis of a customer’s shopping habits consists of looking at what they have bought in the past and recommending more of the same – or similar. Liked reading The Gene by Siddhartha Mukherjee? How about Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari? It’s a direct correlation.
Building a portfolio isn’t like that. There is no direct correlation between one transaction and another. It’s about balance and diversification to mitigate risk. Someone who’s bought a US oil company’s shares probably doesn’t want a stake in a rival company. Instead, to balance the portfolio with the client’s objective in mind, a wealth manager needs to look at investments in different currencies and sectors, for example, and be aware of the potential impact of forex movements, the oil price, regional market moves and much more. And the constant price changes – small and large – mean the portfolio needs regular attention to ensure balance.
All this requires highly-skilled people – which is why wealth managers are highly rewarded. But they derive their insights from clusters of information and averages. It’s like going to a doctor and being informed of your blood pressure based on the answers to questions about your age, diet and lifestyle, and comparing those answers to peer averages, rather than any actual measurement of your blood pressure.
Sophisticated portfolios need sophisticated data analysis to capture the real risk of investment instruments. This means crunching all the daily data overnight – not weekly or monthly – so that wealth managers and brokers can act on up-to-date information. Thanks to cloud computing, we finally have the ability to do this.
Big Data analytics and cloud computing are going to have a huge impact on risk management. Wrong decisions in adverse market conditions will be avoided, helping to build trust. Think of a client with a high-risk appetite and investments in leveraged portfolios. If risk is not accurately captured, unexpected movements could trigger an unnecessary margin call and the liquidation of the portfolio. Accurate risk assessment will mean instruments will behave as expected, leading to fewer over-reactions and more educated decisions.
Much of today’s wealth-management industry is predicated on a myth of careful risk management. Clients trust and believe that the professionals are mitigating risk as effectively as possible. This might have been true 20 years ago, but it’s no longer so today. Managing portfolios in the 21st century without data analysis is like trying to search the web without a search engine. Clients deserve the level of transparency and efficiency smart data analysis brings.
Now that we have the tools to assess risk accurately, it is time the industry overcame its conservative nature and embraced change. It will benefit everyone – investors and managers – brining transparency, cutting imbalanced risk/reward ratio, and gaining trust.
Ruedi Winzeler is Chief Operating Officer at Edgelab, the Swiss investment intelligence fintech