Chapter 5: Transformational Technology

Trends: Technology has long been central to investment management, but its critical role has sometimes helped to calcify processes and preserve substandard systems.

Fear of disruption can be a powerful antidote to innovation. Exponential rates of change and innovation make this untenable, and technology expertise is more important than ever. As we noted in Digitizing the Investor Experience, “…most industry executives agree that not keeping pace with digitization is potentially devastating, with almost three out of four saying that financial services firms not using today’s technologies to transform customer strategies, relationships and experiences are at risk of disruption or obsolescence.”1, 2

Experts at PwC have pointed out that the asset management industry is “a digital technology laggard,” but “technology advances will drive quantum change across the value chain — including new client acquisition, customization of investment advice, research and portfolio management, middle and back office processes, distribution, and client engagement.”3   

FIGURE 1 | Top Priorities for Advanced Analytics Deployment

Top Priorities for Advanced Analytics Deployment

*Includes North Asia, South Asia and Southeast Asia; Source: Ovum ICT Enterprise Insights Survey 2018-19

Significant attention has been showered on systems, but the focus on data is more recent. Virtually everyone is familiar with the potential value of data, but it is still not treated like a precious commodity by many firms. Through force of habit, data acquisition, integration, management, protection, analysis and disposal still often occur in an ad hoc way. Some of this stems from a lack of understanding of what good data management can actually achieve: automation, efficiency, (near) real-time analytics, new insights, quicker decision-making, more effective risk management and more satisfied clients.

This disconnect is finally being resolved as technology and data analytics move from supporting roles to the center stage, thanks to the appeal of monetizing data via investment performance, business development, client retention, risk management or some other application. North American firms are deploying advanced analytics primarily in servicing front-office activities like portfolio management and distribution (Figure 1). Priorities elsewhere might differ. Asian firms, for example, see a more prominent role for analytics in client service, risk management and performance management. European firms fall between these two groups.

Implications: As more and better tools become available, the most pressing question becomes how data can be used for competitive advantage.

Unlocking this potential requires not only the right algorithms, but also the right people. This means more data specialists. Their contributions will not be maximized if they are set apart in dedicated data teams. At a minimum, there will need to be improved coordination between upgraded technology/data teams and other functions such as distribution and investments.

An even better approach is to embed this talent in functional teams, where their expertise can be seamlessly and immediately applied. More firms are also likely to draw on and integrate specialized knowledge from outsourcing partners, systems vendors and data firms to create virtual teams that possess far greater technical acuity than purely internal teams. A growing number of companies are taking the additional step of hiring high-profile senior data scientists, ensuring visibility and representation in the C-suite so data initiatives are considered strategically and prioritized accordingly.

Investment professionals with strong quantitative skills might be forgiven some skepticism about the need for data specialists. They may not have considered that their perception of data is likely outdated. Traditional concepts of data are limited (and limiting). Unstructured and previously inaccessible data sets are now full of promise. A growing number of investment strategies already rely on alternative data from sources as varied as satellite imagery, social media feeds and geolocation. The efficacy of these strategies is open to debate, but there is little doubt that the flood of usable data will continue to grow, sometimes in unpredictable ways.

Quantitative investing is already moving beyond fast-moving, liquid markets. Some institutional investors are attracted to data-driven private equity funds, with the Massachusetts Pension Reserves Investment Trust, for example, recently allocating up to $500 million to such strategies. This could herald the beginning of a profound change in the world of private equity and debt. As pointed out by Greg Bond, the Director of Research for Man Group’s Numeric unit, “ … we’re at an inflection point where we’ll see the quantitative approaches we’ve seen in the public markets come to the private ones. We’ve seen systematic approaches in equities, high-yield, even sports and real estate. Private equity is different, but arguably you can apply systematic principles everywhere.”4

Furthermore, data is being processed in ways that were almost unthinkable until very recently. Natural language processing and machine learning are only two examples of technologies that could fundamentally rewrite not only investment processes but also client interactions. Systems will be required that can integrate more internal and external data to support investment decisions and client reporting that does not yet exist.

Automation and machine learning tools will be recognized increasingly as key sources of efficiency, and flexibility will be prioritized as potentially transformative technologies are never far away. Risk management (client, portfolio, and enterprise) is also poised to evolve quickly, as the industry’s data infrastructure becomes more integrated and information liquidity increases.

Legacy systems are like albatrosses. Platforms of the future will need to be truly agnostic (about everything) so they can be flexible and agile enough to handle any new product, channel, investor or regulatory requirement. As a result, asset servicing will continue to move away from siloed approaches based on archaic systems. In their place will be systems that can effectively manage the stack across more complex and customized investment strategies spanning a growing variety of asset classes. Data integration will happen across the enterprise. Cross-platform collaboration will be key. Managing these systems and processes will be a network of in-house resources, outsourced expertise and software vendors.

1 Digitizing the Investor Experience, SEI, June 8, 2018.
2 Digitizing Financial Services: Mastering Digital Differentiates Leaders From Laggards, Forbes Insights and Cognizant, May 2017.
3 Asset & Wealth Management Revolution: Embracing Exponential Change, PwC, 2019.
4 Quant funds train sights on private equity market, Financial Times, April 15, 2019.
This information is provided for education purposes only and is not intended to provide legal or investment advice. SEI does not claim responsibility for the accuracy or reliability of the data provided. Information provided by SEI Global Services, Inc.
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