Natural language processing and machine learning technologies can enhance the scale and efficiency of data processing and offer investors and managers an informational advantage, says BlackRock’s Sunil Dalal

Source: PEI: Perspectives 2022

Q: As the volume of available data in private markets continues to grow, what challenges and opportunities does that present?

Over time, as private markets have developed and grown in size, so too has the appetite for information relevant to private market investors, as well as the amount of data generated by private market funds. There has been an exponential increase in data on multiple fronts. Meanwhile, institutional investors have put immense pressure on private market managers to provide more transparency around exposures and investment costs in what has been a traditionally opaque market.

In addition to the overall increase in data and data demand, the datasets have also become more complex because they are not restricted to financials that are easily quantifiable. Datasets also extend to a variety of manager or company characteristics that – in theory – should provide insights on things like management style or the level of ESG commitment (when it comes to fund managers) or even consumer sentiment (when it comes to private equity-backed deals). The bottom line is that all of these data sources are disparate, which can create burdensome or even onerous sets of workflows or resource requirements (time, talent, cost).

But make no mistake, there are a lot of competitive advantages just waiting to be uncovered and inform investment decisions. Once data challenges around volume and complexity are solved, I think there are numerous opportunities to generate alpha for investors.

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Ingesting large amounts of data can be tedious, repetitive and taxing. That is where technology needs to step in

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Q: How can emerging technologies help private markets firms address those challenges? 

The investment business is a quest for competitive insights. Simply put, it is an information processing business. And that information – when used in efficient and meaningful ways – can yield new, competitive advantages for both asset owners and asset managers. Asset owners invest significant resources to analyse data so they can select top-performing managers. On the other hand, private market fund managers have to source winning deals and that requires going beyond the referral network and discovering the fundamental value in vast, and often alternative, datasets.

But data processing can be error prone. Ingesting large amounts of data can be tedious, repetitive and taxing. That is where technology needs to step in – to provide efficiency and scale. First and foremost, emerging technologies like natural language processing and machine learning are natural fits that support both the investment process and the hunt for competitive insights.

NLP technology is designed to make humans more productive by eliminating (often) repetitive and tedious tasks such as reading and interpreting an over-abundance of textual language. Computers can extract actionable data points from complex and unstructured business documents, just like the human brain, without the limitations of strain or exhaustion or even human speed.

Relatedly, ML technology is unrivalled in discovering patterns in colossal datasets. It identifies performance factors for both managers and investee companies, while adding value by taking into consideration factors that would be disregarded by human analysts. In that way, these advanced technologies not only contribute to improved efficiency and scale, but also bolster the effectiveness of human analytics teams.

Q: In what ways can investors employ natural language processing technology to help manage the growing data burden? 

Investors are demanding high-quality data that can support the fund manager selection process. However, historically, the information available to them has only been a fraction of what is now out there. Before they commit their capital, they want to have confidence in the potential for alpha generation and, conversely, effective reduction of risk associated with any long-term investment strategies. NLP technology can be applied to unstructured business documents like PDFs or scraped web content, which it can thoroughly index, and make that content searchable. Once they extract the relevant data, investors can use it for any existing workflow and any specific business purpose, whether it is the manager selection process or consuming quarterly reports received from their existing fund managers.

At BlackRock, we have already incorporated this innovative technology into the backend of our analytical platform for private markets – the platform offers quarterly report data services to clients and enables performance and investment monitoring. This automated extraction is unique and can radically improve clients’ workflows by automating and analysing more than 200 data points in every unstructured document. Investors that receive regular inflows of data through quarterly reports can now easily scale their operations by analysing these reports within a specified framework at a set cadence.

Q: How can managers make use of natural language processing technology through the investment life cycle?

Fund managers are facing headwinds in capital deployment. The hunt for investment opportunities has become more nuanced in the pandemic era, where volatility, inflation, the end of a credit cycle, and fragmented liquidity are becoming the norm. But managers can employ NLP to search and extract relevant data points from alternative sources of investment data that might be wholly or partially unstructured.

Customer reviews, social media posts, and even unstructured corporate data, like information on how companies engage with their vendors, are all examples of sources of information that can translate into fundamental value in a deal. Equipped with NLP tools, managers can perform these tasks at scale. They are not only saving time spent on fundamental research, but they also benefit from NLP’s ability to screen deals in a comprehensive and automated way. In this way, NLP technology secures a healthy and consistent dealflow for fund managers.

Upon capital deployment, NLP can be put to use in value creation. In processing large amounts of publicly available data, combined with the unstructured corporate data of a target company, this technology can help managers quickly understand broader market trends, the competitive landscape, and trends in product and consumer sentiments – and not just as a momentary snapshot. Essentially, NLP can flag any sentiment shifts over time or profitability inflection points, allowing managers to act with speed and efficiency.

Q: What are the main considerations for private markets firms when it comes to introducing emerging technologies into their operations?

The number one consideration is a vendor relationship with a proven champion of innovation. Both investors and private markets firms must have

consistent vendor support to preserve their competitive and informational advantage through technology-enabled manager selection or deal origination processes.

On an operational level, the choice of technology should be driven, in our view, by recalibration potential. Meaning, a strong ability to pivot to different contexts or content types. Many of the available NLP solutions currently in the market are trained to solve for just a single, specific business need and are architected according to a pre-defined industry or operational workflow. Private markets firms should consider a widely applicable NLP technology that is next gen in its ability to be context- agnostic and run on any type of document, while immediately ingesting relevant data (without the requirement to manually tune for the range of workflows in the investment life cycle).

Q: How has the take-up of emerging technologies among private markets firms evolved in recent years? 

For several years now, we have observed nimble investment firms deploying NLP and ML in their hunt for investment opportunities. But the adoption of these and other emerging technologies also depends on several factors such as the size and the stage of the firm, as well as geographical or industry focus.

Based on the firm’s experience, large firms with broadly defined investment mandates originate deals from a global pool of potential targets and struggle to leverage technology to scale their deal origination functions. Comparatively, private equity firms specialising in early- stage companies are choosing from a large number of new entrant companies with significant failure rates. In both instances, technology can enable firms to scan target businesses, run predictive analyses, and benchmark them against the rest of the industry and identify viability.

Local and small private markets firms, on the other hand, rely more on local knowledge and familiarity with the local macroeconomic environment. However, even on that front, we believe there is still an abundance of opportunity to eliminate manual processes, ingest data efficiently and with speed, and empower human teams to make better decisions.

Sunil Dalal is managing director and head of alternatives engineering within the Aladdin Product Group at BlackRock