Harnessing the power of big data

The apparent ease with which online retailers target clients and identify consumer trends through behavioural and pattern analysis, and plan their inventories accordingly, is impressive. Many fund managers are also realizing the importance of big data in raising capital and some have used sophisticated analytics to spot investment trends and lucrative trading opportunities.

Quantitative-focused fund managers comb social media sites such as Twitter, Facebook and LinkedIn to gauge positive or negative user sentiment, then trade off the back of that data. Others are deploying satellite imagery to assess economic development in emerging markets. These satellite images can enable managers to identify significant construction or building activity in urban areas which, combined with other data can help to assess potential economic opportunities.

The success of organizations such as Amazon and Netflix has been driven by their ability to customize services through algorithmic-based analysis. These technology giants should serve as an inspiration for fund managers.

Regulatory developments are also helping make stronger analytics possible in the fund management sector. Under the Markets in Financial Instruments Directive II’s (MiFID II) product governance obligations, the necessary client information may be available to help fund managers develop and offer more bespoke services.

In June 2017, Paul Stillabower, managing director and global head of product management at RBC Investor & Treasury Services, participated in a panel discussion at FundForum International that looked at big data trends and what the funds industry is doing to better leverage the information it holds.¹ The panel, which included technology vendors and fund managers, discussed some of the operational challenges that come with big data collection and its use.

Regulatory kickstart to big data

The FundForum panelists observed that big data is also being used as a tool to improve client engagement and sales processes, which has been further enabled through transparency regulations such as MiFID II. The directive’s strong product governance provisions require investment managers to be certain that their product offering is suitable for their target market. Banks, online platforms, and other distributors have said that they will supply information to manufacturers about end clients, which is a significant ‘data coup’ for firms. In 2017, banking was seen as one of the largest industries making significant investments in big data.²

The success of organizations such as Amazon and Netflix has been driven by their ability to customize services through algorithmic-based analysis

By leveraging MiFID II data, fund managers will be able to assess why particular segments of society are accumulating or decumulating exposures to specific asset classes or strategies. Such data could give managers a regional or demographic breakdown of buying patterns, and allow them to distribute products more aligned with data-driven insights, instead of adopting a broad-brush approach.

Stillabower points out that technological innovation and a clear strategy incorporating big data analytics may also help fund managers in their efforts to bring millennials into their fold. Millennials are a target demographic that have generally shown disinterest in personal finance. Fund managers, as a collective group, need to find a way to attract them, particularly given the potential for large wealth transfers from baby boomers to millennials.

Big data complexities

Big data will only be relevant and useful if it is incorporated into the daily business strategy of fund management groups. Panelists acknowledged that data is derived from a number of different sources and much of it is unstructured, which raises immediate quality control concerns.

Big data will only be relevant and useful if it is incorporated into the daily business strategy of fund management groups

Data shortcomings could be exposed through robo-advice, in which end investors provide information about their risk tolerance and return expectations, and then an automated advisor identifies solutions based on the tolerances and objectives. This digitization of fund selection has clear benefits such as scalability and lower costs, but its current configuration could create a process under which client money is directed to low-cost passive products. In a volatile market, this approach may not necessarily be conducive to the creation of balanced, risk-adjusted portfolios, which may require actively-managed investment diversity and not simply equity market benchmarking.

“Passive fund products have enjoyed a perfect financial backdrop to drive out-performance, and growth. These strategies are benefiting from strong investor demand. Although passive funds are not limited to the equities market, a potential risk for end investors is the impact of a large correction in this sector. In that scenario, investors in ‘tracking funds’ would stand to lose an equivalent amount of capital, and may then reassess the advisors – robo or real – that directed them to those funds. Big data and digital strategies will take time to get right," said Stillabower.

Tools versus talent

Many fund managers and banks operate using legacy technology systems. Firms will continually invest in technology and frequently build new platforms to supplement or wrap around older ones. In this context, big data may not be a natural fit.

Big data and digital strategies will take time to get right

Stillabower commented that, “A lot of organizations work with old technology and digitizing these systems efficiently is hard. It requires time and effort, particularly as there is so much more data available and accessible in the ecosystem."

A key takeaway from the discussion was that fund managers must ensure they have the right resources in place as soon as possible. One panelist cautioned that poor big data management could result in a siloed approach to data analysis, and the reality is that many fund management houses may not yet have the necessary talent pool at their disposal, such as big data experts and data scientists. The conclusion of the panelists was that financial institutions need to review big data at an enterprise level and not simply delegate it to their IT personnel. Training an equity or fixed income analyst to become a master of data is probably not a practical approach.

Sources

1. FundForum panel (June 12, 2017) "Harnessing the power of big data: the business case"
2. International Data Corporation (IDC) (March 14 2017) Big Data and Business Analytics Revenues Forecast to Reach $150.8 Billion This Year, Led by Banking and Manufacturing Investments, According to IDC