Digital transformation could solve data fragmentation in the private markets

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Private markets have an outsized impact on global capitalism. They move trillions every year to funds and investments, often steering them into high-tech development ventures. Yet, the funds themselves are underinvested in technology, investing just a third to half of what public-facing financial institutions commit to innovation as a percentage of their revenue. The resulting hangover of legacy methods has hampered the investor experience and data management from the inception of most funds. This bottleneck – at the very point where capital flows in – has confounded both investors and fund managers and persisted through the funds’ lifecycle. 

The pain (symptom) and underlying causes (data fragmentation) 

Private markets, an engine of investment in tech innovation, have been overdue for digital transformation of their critical activities related to raising capital and fund management. Deal execution and compliance also depend on those processes. Virtually every participant — from investors (limited partners, or LPs) to fund managers (general partners, or GPs) and their lawyers and fund administrators — has felt the inefficiency of archaic paperwork when onboarding investors. Relying on PDF forms, Excel spreadsheets, and manual processes has turned more problematic recently, thanks to a talent shortage that coincides with the need to scale for a wider LP market that includes retail investors.

Post-COVID-19, more funds have accelerated their adoption of workflow automation and this is a major step ahead, but not the entire solution. That’s because a major obstacle to optimizing fund formation and relationships with LPs is in the longstanding sediment layers of discoordinated data on which the industry runs. Investors, regulatory authorities, each fund or fund family, and different portfolio companies all structure and see their data differently. 

Meeting that challenge is a complex exercise in strategic architecture choices and data “translation.”

Modernizing private markets, starting with fund formation

Process automation can radically improve the experience of investors, reduce their data entry errors, meet compliance requirements, and manage the LP life cycle. Workflow to collect required information replaces onerous, friction-marred sequences to qualify and onboard investors. In addition, it guides investors through entering their information correctly and performs data integrity checks. Funds can cut onboarding time and friction, speed up fund formation, and provide the red carpet experience their investors expect. Now, when private equity investments have slowed, this is compelling for fund managers. 

As it does in many industries, an automated platform can capture and validate data once, hand it off automatically and avoid transcription errors. This reduces processing costs, but also improves the data quality and throughput further downstream.

Meet data disparity head-on or halfway?

Once fund operations are up and running, it’s apparent that each fund has its own data model, and portfolio companies have their own structures for reporting results. An industry-wide standardized data protocol would be the ideal solution for private markets, but it’s also elusive and will require agreement across numerous actors. That means it’s up to practitioners and software vendors to adopt tools and methods to normalize data and work around the fragmented, disparate data structures. Building this kind of platform calls for careful architecture tradeoffs between being prescriptive (“our way, or no way”) versus more adaptive (“your way, when necessary”).

A workflow solution needs to balance a standardized, set approach against the ability to customize and match specific funds’ practices. Larger funds, in particular, tend to require more customization. Keep in mind that a solution will need to flex to match changing compliance requirements; it’s imperative to verify that every investor is qualified and meets SEC requirements and keep the fund in compliance with its fiduciary obligations to investors.

Newer technology will contribute to private market solutions

No fund manager wants to be left behind as expectations rise, and workflow platforms provide a common starting point, particularly if they embed domain-specific business logic. Cutting-edge technologies are likely to be integrated into private markets as they embrace digital transformation.

  • Blockchain may end up serving as an ‘industry ledger’ for transactions across private markets, in the future. It is also likely to be helpful in both KYC and AML, reducing unnecessary replication of data, making it easier to trace financial transactions, and helping push toward clear, uniform requirements for due diligence. There is already some experimentation with blockchain for securities transactions. For blockchain to hold a major role in private markets depends on funds adopting a standardized data protocol. Such a protocol is an elusive holy grail for the industry.  Blockchain technologies also need to mature further and overcome well-documented deficiencies in performance, scalability, etc.
  • RPA (robotic process automation) can help modernize how funds interface with their LPs in areas beyond qualification and onboarding. RPA tools are essentially bot programs which can automate routine tasks that run on outdated legacy systems. In funds, these essential processes cannot be easily retired or replaced – and so can be automated by RPA. Lean back-office operations can save much time by applying RPA to mundane tasks, freeing up resources to handle higher order work. Ultimately, RPA bots that are trained in the private market vertical can help offload aspects of the GP/LP relationship, including batch routing transactional paperwork and collating monthly reports.
  • AI and ML may further unlock the power of RPAs by injecting smarter analysis and understanding into the picture. AI can make judgment calls and direct orders to the workhorse bots, amplifying their impact and adding use cases to handle more complex scenarios. AI should excel at parsing and sifting through large volumes of data at lightning speed–so long as the data has been collected. The classic problem for AI is always how to ensure data is ready, and requires extensive data collection and rigorous human training. These daunting prerequisites can often be overlooked when AI systems are deployed inside organizations. With enough access to data from across the industry, AI-driven systems are expected to strengthen compliance, diligence and KYC/AML from the back office, and provide powerful dynamics for seeking deal opportunities from the front office.
  • Low-code and no-code (LCNC) solutions allow platform updates and customization to match fund-specific processes, without relying on software developers. Current legacy solutions are rigid, monolithic, and often hard-coded, making them difficult or impossible to update to meet contemporary standards. These tools help address the data normalization challenge as new funds, portfolio companies and features are added to digital transformation initiatives.

    For certain internal workflow use cases, LCNC offers the promise of rapid configuration and deployment of pre-engineered software modules. With limited or no programmer resources, business or IT specialists can spin up basic standalone applications for processing investor data and documentation on the backend. This comes with the caveat that no-code programs would be less portable or scalable; have difficulty with edge cases; and be risky if interfacing directly with external customers. Given the right resources, a combination of both low-code and no-code solutions may be able to bridge some reporting and compliance gaps between legacy processes and present-day demands for running a fund.

By taking the first step in digital transformation – workflow automation – private market funds are fundamentally improving how they operate, taking friction and lost time out of the investing process. At the same time, data quality and confidence in compliance have improved, along with investor satisfaction. Going forward, adaptable architecture and multilayer data translation using new technologies can continue the gains that private market funds have achieved in the first phase of innovation.

Alin Bui is the cofounder and Chief Strategy Officer at Anduin.

Originally appeared on: TheSpuzz