Developments in financial technology continued to pick up pace in 2018, with open banking, artificial intelligence (AI), and distributed ledger technology (DLT) capturing financial news headlines and claiming a new era for asset managers and their asset servicers. From wider adoption of AI to sophisticated data solutions, technology is helping to improve control, drive efficiencies, and provide greater insight into client behaviors. But not all of these developments live up to the hype: many recently- heralded technologies are merely a solution looking for a problem, not to mention the fact that several, like process automation and scalable data processing, have been industry stalwarts for years.
2019 will be a year in which the applications of purported paradigm-shifting technology will be tested. 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. Smart application of new technologies is allowing firms to begin harnessing data in new ways and those who can do this well (and safely) will stand out from the crowd. Work culture is changing, too, as firms look outside of their industry for inspiration and specialized talent, redefining roles that have existed for decades.
Here are the trends we believe will shape asset management and asset servicing this year, and in the years ahead:
1. Artificial Intelligence: 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. The result is higher accuracy and significant reductions in analyst workloads as daily exception reviews are reduced and eliminated.
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.
2. 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 and servicers 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. 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. While we are still in early days of adoption, early results indicate this is a promising trend worth watching.
3. Blurring of Lines Between Technology and Operational Roles
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 and servicers to consider as they hire data wranglers, data engineers, 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 most transformational solutions are likely to come from unbiased, technology agnostic solutions architects that can go deep on business matters and see a range of possible approaches. Successful organizations will pay attention to getting this balance of skills and capabilities right.
4. Technology and 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. 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.” Building capabilities in design thinking, agile mindset, influencing, and team building should remain at the top of the list of learning content investments in 2019.
5. The World’s Most Valuable Asset
As we move closer to a world of transparency, connectivity, and aggregation, one component fuels the engine: Data. That’s why The Economist dubbed data as “the world’s most valuable asset.” As technology offers greater access to more accurate and independent data, even with the right workforce, 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.
The question here is how long it will take for the regulatory expectations around data providers to catch up with those of financial services providers, where there’s a century’s worth of protection that customers can rely upon. Banks and asset managers are experts in operating in a fiduciary capacity and within a variety of regulatory frameworks. Their data providers, however, may not be. Will this be the year that asset managers look for more comprehensive data services providers willing to perform at the same fiduciary level?
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