By Julian Trostinsky, Director, Global Support and Business Development, Gresham Technologies
There has been a significant amount of volatility and uncertainty in financial markets over the past year – this will not be breaking news for anyone. And it doesn’t seem to be over. Financial institutions are feeling the pressure as increased volumes strain their operational teams amid a major talent shortage that will only get worse.
Businesses need to act and respond faster to market movements and customer demands while complying with ever-changing regulations. With low to negative returns in the current environment, operations teams are also being pressured to control or cut costs and still show the value of customer service and risk reduction, as companies need to justify their fees, protect their assets and prepare for the future.
In all of this, buy-side companies are still struggling to gain efficiencies across a host of critical business areas. Across the industry, a lot of money is wasted on inadequate and disparate data, as well as outdated processes often supported by legacy systems. This can lead to considerable stress given the current economic volatility, leading many asset managers to change their investment habits.
In search of higher returns
The trend of asset managers turning to alternative asset classes in search of higher returns is not new. But the volatility of more vanilla assets this year, such as listed stocks and sovereign bonds, has thrust them into the spotlight. Indeed, US investors are bypassing private equity deals in the UK due to the fall of the pound, while it was recently reported that Goldman Sachs was leading a host of asset managers in the pursuit of alternative asset transactions with struggling pension funds in need of more liquidity in their portfolios. However, there is an operational problem here.
Growing data pressure
As new assets come into play for an increasing number of buy-side companies, the amount of data they need to consume increases exponentially. On top of that, they find they have to deal with new types of data. In short, data is needed faster than ever in more functions, and it’s getting more and more complex. Data streams lack consistent format standards and are unpredictable. Things change frequently and without warning.
Valuing operational talents
Tracking all the flows and then normalizing the data and distributing it across multiple functions requires huge resources in terms of infrastructure and staff time. The mindset shift that needs to happen for asset managers is to see the operations team as a revenue-generating potential rather than a cost.
If staff are to be empowered to focus on higher value work that differentiates the business and uncovers opportunities to generate new revenue, they must be freed from data load and other routine tasks. All of this becomes an even bigger problem when you consider the current state of the labor market. The ultra-competitive hiring environment that buyers currently face, in the midst of “the great resignation”, poses challenges for operations teams. The talent shortage only seems to be getting worse as companies compete to fill vital positions.
Streamline operations for better results
The real problem from a back-office perspective is when you need to recruit more skilled staff who are already spending time on high-volume, low-value processes. While these processes are critical to the smooth running of day-to-day business operations, it’s a waste of time and resources to have talented operations teams spending half their days working on them. On top of that, it’s likely to be a key reason people leave operational teams – to seek more challenging work.
This is where the value of a managed services model comes in, as it allows teams to focus on their core business while ensuring that the essentials are handled.
Ultimately, companies that can optimize their operations for speed, scale, and resilience can focus more on their core competencies. This means that they are the ones who will find opportunities in these times of economic uncertainty.
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