As budgets run short, data can diagnose inefficiencies, then fix them

For asset managers not in the business of deploying robust data systems themselves, service providers can handle their data and optimising efficiencies using AI, machine learning and other tools can analyse to gain insights, writes Bob Balfe, chief technical officer at 4Pines Fund Services.

By Bob Balfe

Read the original article here.

If you don’t know what’s wrong, you can’t fix it. Similarly, private capital leaders need to know where their operations are inefficient. Otherwise, they can’t address their pain points. That’s especially true as we look down the barrel of a recession and try to figure out how we can tighten our belts. Headcount is one thing. But what about private capital data operations?

Once they identify bottlenecks and redundancies, however, fund managers today can pick from many innovative technological solutions on the market that can help them formulate the key performance indicators that will help them reduce friction and accelerate operations.

Yet asset managers don’t necessarily have the capital, agility or, frankly, the will, to deploy robust data systems themselves. And it’s no wonder why. They manage funds. They are not fund service providers. But the latest fund services technology has what they need: tools to identify and ease the pain points in their workflows while also reporting, tracking compliance and other rules and, most importantly today, analysing and drawing lessons from the many streams of information related to their portfolios and organisations.

That’s why more funds are turning to professional service providers that can integrate the best data tools and comprehensive tech solutions into a single platform.

Rather than piecing together a patchwork of solutions, fund managers can sleep easy knowing a single, trusted partner is handling their data and optimising efficiencies. These transparent arrangements allow the fund manager to “insource” data operations – inviting a collaborator into their organisations to oversee their data – offload the risks associated with technology and spend more time focused on capital raising and creating investment products.

The key benefit of these centralised platforms is that they generate massive amounts of data that AI, machine learning and other tools can analyse to gain insights about a business’s workflows. These internal data warehouses, or lakes, contain the raw material for insights that empower asset managers and investors alike.

Awareness of innovation and client demands for real-time, frictionless services are compelling fund managers to offer top-notch tech solutions. We’re already hearing about due diligence meetings where investors are raising concerns about comparative inefficiencies because if they feel as if fund managers are delaying the adoption of advanced technology.

It is therefore crucial for funds to look inward to identify and measure inefficiencies and opportunities for tech solutions, with KPIs serving as the criteria and test for those solutions. That means collecting and dissecting a lot of data – understanding the myriad factors that affect portfolios, how their employees spend their time, which tasks can be automated and how tech can help humans work faster or more effectively.

A full support platform contains both the diagnostic tools to identify pain points and configurable tools to help resolve them via a close hewing to progress in KPIs. The best platforms, moreover, include intuitive displays that make information immediately accessible for whoever needs it in a given organisation.

If, for example, an asset manager can view the history of capital calls, they can also view how, when and why delays occurred in the documentation and other processing necessary to finalise those calls. They can then create new KPIs, based on what’s possible, using advanced technology as new problems and demands arise as they inevitably will.

When organisations are able to break down workflows with this level of detail, they are able to make many small improvements that together create significant savings in time and money. McKinsey recently estimated that AI could save banks alone worldwide as much as $1 trillion annually, for example.

Similarly, the World Economic Forum and others conducted a “Global AI in Financial Services Survey” and found that 77 percent of all respondents anticipate “AI to possess high or very high overall importance to their businesses within two years.” Almost 64 percent expect that AI will generate new revenue in areas, including process automation.

But it’s up to fund managers — with guidance from internal experts and outside partners — to identify the processes, tasks and roles that will most benefit from these new technologies. Once they recognise the pain points that need fixing, leaders can address them, improve efficiency and, hopefully, sales, too, as the business progresses.

Next, leaders can use their data for forecasting, planning and mitigating risk using historical records that provide clear lessons for better performance in the future.