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Private markets have an excessive influence on global capitalism. They move trillions into funds and investments every year, often sending them to high-tech development companies. Yet the funds themselves are underinvested in technology, investing only a third to half of what public financial institutions invest in innovation as a percentage of their revenues. The resulting hangover from legacy methods has hampered the investor experience and data management of most funds from the start. This bottleneck – right at the point where capital flows in – has confused investors and fund managers alike and persisted throughout the fund’s life cycle.
The pain (symptom) and underlying causes (data fragmentation)
Private markets, a driver of investment in technological innovation, have been overdue for the digital transformation of their critical capital raising and fund management activities. 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 attorneys and fund managers — has felt the inefficiency of archaic paperwork when hiring investors. Relying on PDF forms, Excel spreadsheets and manual processes has become more problematic of late, thanks to a talent shortage coinciding with the need to scale up for a broader LP market that also includes retail investors.
After COVID-19, more funds have accelerated their adoption of workflow automation and this is a big step forward, but not the complete solution. That’s because a major obstacle to optimizing fund-building and relationships with LPs is the long-standing sediment layers of uncoordinated data. on which the industry runs. Investors, regulators, each fund or family of funds, and different portfolio companies all structure and view their data differently.
Meeting that challenge is a complex exercise in strategic architecture choices and data translation.
Modernize private markets, starting with fund-raising
Process automation can radically improve the investor experience, reduce data entry errors, meet compliance requirements and manage the LP lifecycle. Workflow to collect the required information replaces cumbersome, friction-marred sequences to qualify and onboard investors. In addition, it guides investors in correctly entering their information and performs data integrity checks. Funds can reduce settling time and friction, accelerate fund building, and provide the red carpet experience their investors expect. Now that private equity investment has slowed, this is compelling for fund managers.
As in many industries, an automated platform can capture and validate data once, transfer it automatically, and avoid transcription errors. This reduces processing costs, but also improves data quality and throughput down the chain.
Meeting data inequality frontally or halfway?
Once fund operations are up and running, it is clear that each fund has its own data model and portfolio companies have their own performance reporting structures. An industry-wide standardized data protocol would be the ideal solution for private markets, but it is also elusive and requires agreement between different actors. That means it’s up to practitioners and software vendors to use tools and methods to normalize data and get around fragmented, disparate data structures. Building this kind of platform requires careful architectural trade-offs between prescriptive (“our way, or no way”) and more adaptive (“your way, if necessary”).
A workflow solution must strike a balance between a standardized, established approach and the ability to adapt and align the practices of specific funds. Larger funds in particular require more customization. Keep in mind that a solution needs to be flexible to meet changing compliance requirements; it is imperative to verify that each investor is qualified and compliant with SEC requirements, and to ensure that the fund is meeting its fiduciary obligations to investors.
Newer technology will contribute to private market solutions
No fund manager wants to be left behind as expectations mount, and workflow platforms provide a common starting point, especially if they embed domain-specific business logic. Advanced technologies are likely to be integrated into private markets as they embrace digital transformation.
- Blockchain may in the future serve as an ‘industry ledger’ for transactions in private markets. It is also likely to be useful in both KYC and AML, reducing unnecessary data replication, making it easier to track financial transactions, and aiding in the pursuit of clear, unified due diligence requirements. There is already experimentation with blockchain for securities transactions. For blockchain to play an important role in private markets, funds must adopt 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 the way funds communicate with their LPs in areas beyond qualification and onboarding. RPA tools are essentially bot programs that can automate routine tasks that run on legacy legacy systems. In funds, these essential processes cannot be easily withdrawn or replaced – and thus can be automated by RPA. A streamlined back office operation can save a lot of time by applying RPA to mundane tasks, freeing up resources to handle higher order work. Ultimately, RPA bots trained in the retail vertical can help offload aspects of the GP-LP relationship, including batch routing transaction papers and monthly report collection.
- AI and ML can further unlock the power of RPAs by injecting smarter analysis and understanding into the picture. AI can make decisions and route orders to the workhorse bots, increasing their impact and adding use cases to handle more complex scenarios. AI must excel at breaking and searching large amounts of data at lightning speed, as long as the data is collected. The classic problem for AI is always how to ensure data is ready and requires extensive data collection and rigorous human training. These discouraging conditions can often be overlooked when AI systems are deployed within organizations. With ample access to data from across the industry, AI-powered systems are expected to strengthen compliance, due diligence and KYC/AML from the back office and provide powerful dynamics for deal-seeking from the front office.
- Low code and no code (LCNC) solutions enable platform updates and customizations 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 today’s standards. These tools help address the challenge of data normalization 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 implementation of pre-designed software modules. With limited or no programming resources, business or IT specialists can build standalone core applications for processing investor data and documentation on the backend. This comes with the caveat that programs without code would be less portable or scalable; have difficulty with edge cases; and risky if you contact external customers directly. With the right resources, a combination of both low-code and no-code solutions can potentially bridge some of the reporting and compliance gaps between legacy processes and the current requirements for running a fund.
By taking the first step in digital transformation – workflow automation – private market funds are fundamentally improving how they operate, removing friction and lost time from the investment process. At the same time, data quality and confidence in compliance have improved, as has investor satisfaction. In the future, adaptable architecture and multi-layered data translation using new technologies can continue the gains private market funds have made in the first stage of innovation.
Alin Bui is the Co-Founder and Chief Strategy Officer at Anduin.
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