A year of pragmatic transformation

Simone Vroegop, Head of European Product Management and Diane Teed, Managing Director, Investor Services, BrownBrothersHarriman discuss how the past number of months has seen a year of practical change.

Across the asset management industry, technology is helping to improve control, drive efficiency, and provide greater insight into client behaviors. If the wider adoption of open banking, artificial intelligence (AI), Robotic Process Automation (RPA), and distributed ledger technology (DLT) is any indication, great change is afoot for asset managers.

But not all of these developments live up to the hype: many recently-heralded technologies are solutions looking for problems, not to mention the fact that several, like process automation and scalable data processing, have been industry stalwarts for years.

Time will tell which technologies will stand out as sustainable industry solutions, and which are short-lived fads, but one thing is certain — the industry is evolving.

Here we take a look at how technology is reshaping asset management, focusing on five key developments.

AI: not when, but how

Many businesses around the globe are look­ing at how they can use AI to improve their processes. A recent McKinsey poll of more than 2,000 companies found nearly half had embedded at least one AI capability into their standard business processes, while another 30% said they were piloting the use of AI.

AI is already improving the value chain for many asset managers – several have incor­porated AI into their front-office systems to identify potential trades and spot human error. In the back office, AI is delivering efficiencies in areas that have historically relied on people to identify, analyze, and resolve exceptions.

For instance, BBH has seen natural language processing in a supervised machine learn­ing framework categorize cash breaks and other exceptions, and then bring automated resolution rates upwards of 95%. In fund valuations, we’ve used predictive analytics and machine learning to eliminate false pric­ing exceptions and pinpoint true anomalies, resulting in higher accuracy and significant reductions in analyst workloads.

Looking ahead, the big question for our in­dustry is not “if” or “when” to invest and apply AI, but “how.” The full power of this technology comes from the ability to see, predict, and learn across vast sources of in­formation that would otherwise be impossi­ble to absorb. Structured and controlled data is a pre-requisite. Asset managers who get that foundational component right – either on their own or with the right partners – will have a competitive advantage.

Smarter use of automation

Despite a widely held belief that Robotic Process Automation (RPA) is the path to productivity and cost reduction, many firms are finding the returns of stand-alone RPA to be overstated.

First, consider its use cases: RPA is often focused on process automation, eliminating a single manual task. Put another way, RPA typically replaces something fin­gers touch – rote data entry, repetitive tasks, data collection, or other manual processes.

While RPA can efficiently execute specific tasks with a high degree of accuracy, its ap­plication is not designed to address bigger systemic problems. And since RPA programs usually interact with legacy systems, even minor changes to those systems may lead to a broken process.

If a stand-alone RPA program is not properly managed – which means selecting the right projects and en­suring smart configuration and infrastructure control – asset managers may find the results will fall short, compared with other technology solutions.

There is greater promise of transformation when RPA is used in combination with next-generation AI tech such as machine learning and natural language processing. By integrat­ing cognitive capabilities into RPA, platforms can automate subjective and judgment-based tasks on top of the rote manual tasks the RPA is already doing.

"Many businesses around the globe are look­ing at how they can use AI to improve their processes."

In effect, AI may be able to carry the analytical legwork that typically requires significant time and human-power. The applications for our industry could be pro­found. Consider trade settlement, where to­day many firms are using robots to monitor DTC pending queues, sort client instructions, resolve unknown trades, or move items to separate queues to await human instruction.

This can be a simple yet time-consuming task, and automation not only allows ana­lysts to focus on genuine issues, but relays errors back to the counterparty. In the future, instead of a human analyzing and resolving exceptions, AI will.

Blurring the lines between tech and ops

As firms begin to employ new and emerg­ing technologies in their operations, we see a blurring of traditional lines between opera­tions and technology roles. Simply put, the days of an operating area identifying a problem and handing it over for IT experts to solve are over.

Transformation requires integrated think­ing. And the proliferation of self-service and “low code” tools requires different governance models as well as new skills and competencies across the enterprise.

In the new models, business experts need to more deeply understand their data architecture, relevant technologies, and their capabilities; technologists need to more deeply understand business challenges to select the right technol­ogy tools and recommend the best solutions. This is especially important for asset manag­ers to consider as they hire data wranglers, data engineers and data scientists, and what we see as the most valuable role – solu­tions architects.

If data expertise is not paired with subject matter expertise and a broad un­derstanding of technology options, these roles may bring little value.

The future of work

Emerging technology is solving real operating problems and elevating the value firms can provide to their clients, which is increasingly important as the industry faces significant cost pressure.

But driving to zero cost does not mean driving to zero people – exceptional talent is required to ask the right questions, teach the machines, and drive continuous val­ue creation.

"As we move closer to a world of transparen­cy, connectivity, and aggregation, one com­ponent fuels the engine: Data."

Transformation is most helpful if it’s pragmatic and helps achieve business goals. It has to be about understanding prob­lems and finding the right solutions before solutions are implemented. Firms will con­tinue to need designers, thinkers, and trans­formers — and more of them.

A culture of continuous transformation requires that we overcome the unconscious biases that prevent us from seeing a different way forward. Learning how to challenge current thinking and how to move ideas through an organization are the most important workforce competencies in the “workforce of the future.”

Data as an asset

As we move closer to a world of transparen­cy, connectivity, and aggregation, one com­ponent fuels the engine: Data. As technology offers greater access to more accurate and inde­pendent data, challenges will arise.

Most notably, aggre­gating data from multiple sources, providing real-time access to it, and gleaning insights from disparate data sets that can be put to action quickly. Above all, a good data gover­nance model is paramount to ensuring data is consistent, valid, and protected.

The safekeeping of data is also of utmost im­portance and high-profile data breeches re­veal what can go wrong when data gets into the wrong hands. This is especially true in financial services. Investors trust asset manag­ers with their assets, who in turn trust asset servicers with theirs.

That trust is backed by experience and a regulatory driven fiduciary obligation. When it comes to data, however, more firms are using third party data provid­ers which are not currently held to the same standards as financial firms.

So, how long it will take for the regulatory expectations around data providers to catch up with those of financial services providers?

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