Private markets have a huge impact on global capitalism. Trillions die each year in funds and investments, often driving them into high-tech development initiatives. However, the funds themselves are underinvested in technology, investing only one-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 investor experience and data management from the very beginning of most funds. This bottleneck – right where capital flows – confused both investors and fund managers and persisted throughout the life cycle of the funds.
Pain (symptom) and underlying causes (data fragmentation)
Private markets, the engine of investments in technological innovation, are lagging behind in the digital transformation of their critical activities related to raising capital and managing funds. The execution and compliance of agreements also depend on these processes. Virtually all participants – from investors (limited partnerships or LPs) to fund managers (general partners or GPs) and their attorneys and fund administrators – felt the inefficiency of archaic paperwork during investor placement. Relying on PDF forms, Excel spreadsheets and manual processes has become more problematic recently, thanks to a talent shortage that coincides with the need to scale for a larger LP market that includes retail investors.
After COVID-19, more funds have accelerated the adoption of workflow automation and this is a major step forward, but not the whole solution. This is because a major obstacle to optimizing fund formation and LP relationships is in the long-standing sedimentary strata of disordered data. on which the industry turns. Investors, regulators, each fund or family of funds and different portfolio companies structure and view their data differently.
Tackling this challenge is a complex exercise of strategic architecture choices and data “translation”.
Modernizing private markets, starting with the establishment of funds
Process automation can radically improve the investor experience, reduce data entry errors, meet compliance requirements, and manage the LP lifecycle. The workflow to gather the required information replaces the burdensome and frictionalized sequences of qualifying and integrating investors. It also guides investors in entering their information correctly and performs data integrity checks. Funds can reduce lead time and attrition, accelerate fund formation, and deliver the red carpet experience their investors expect. Now, when private equity investments have slowed, this is attractive to fund managers.
As in many industries, an automated platform can capture and validate data once, transmit it automatically, and avoid transcription errors. This reduces processing costs, but also improves data quality and throughput further downstream.
Satisfy the data disparity head-on or in the middle?
Once the fund’s operations are up and running, it is evident that each fund has its own data model and the companies in the portfolio have their own structures for reporting results. An industry-wide standardized data protocol would be ideal for private markets, but it is also elusive and will require agreement between numerous players. This means that it is up to software professionals and vendors to adopt tools and methods to normalize data and bypass fragmented and disparate data structures. Building this kind of platform requires careful architectural trade-offs between being prescriptive (“our way, or not”) and more adaptive (“your way, when needed”).
A workflow solution must balance a standardized and set approach with the ability to customize and match specific fund practices. Larger funds, in particular, tend to require more customization. Keep in mind that a solution will need to be flexible to meet changing compliance requirements; It is imperative to verify that each investor is qualified and meets SEC requirements and to keep the fund compliant with its fiduciary obligations to investors.
The latest 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 incorporate domain-specific business logic. Cutting-edge technologies are likely to be integrated into private markets as they embrace digital transformation.
- Blockchain it could end up serving as an “industry ledger” for private market transactions in the future. It is also likely to be useful in both KYC and AML, reducing unnecessary data replication, simplifying the tracking of financial transactions, and helping push towards clear and uniform requirements for due diligence. There is already some experimentation with blockchain for securities transactions. The fact that blockchain plays an important role in private markets depends on the adoption of a standardized data protocol by the funds. 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 that can automate routine tasks performed on legacy legacy systems. In funds, these essential processes cannot be easily withdrawn or replaced, and therefore can be automated by RPA. Lean back-office operations 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 private market vertical can help download aspects of the GP / LP relationship, including batch routing of transactional paperwork and collecting monthly reports.
- AI and ML can further unlock the power of RPAs by injecting smarter insight and insight into the picture. AI can make assessments and direct orders to work robots, amplifying their impact and adding use cases to handle more complex scenarios. AI should excel at analyzing and sifting through large volumes of data at the speed of light, provided the data has been collected. The classic problem for AI is always how to ensure that data is ready, and it requires extensive data collection and rigorous human training. These daunting prerequisites can often be overlooked when AI systems are implemented within organizations. With sufficient access to industry-wide data, AI-powered systems should enforce 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 Solutions (LCNC) allow platform updates and customization to suit fund specific processes, without relying on software developers. Current legacy solutions are rigid, monolithic, and often hard-coded, making them difficult or impossible to upgrade to meet contemporary standards. These tools help address the challenge of data normalization as new funds, portfolio companies and features are added to digital transformation initiatives.For some internal workflow use cases, LCNC offers the promise of rapid setup and deployment of pre-engineered software modules. With limited or no programming resources, business or IT specialists can build basic standalone applications for data processing and investor documentation on the backend. This comes with the caveat that programs without code would be less portable or scalable; have difficulty with borderline cases; and be risky if it interfaces directly with external customers. Given the right resources, a combination of low-code and no-code solutions may be able to bridge some reporting and compliance gaps between legacy processes and current fund management demands.
By taking the first step in digital transformation – workflow automation – private market funds are substantially improving the way they operate, eliminating friction and wasted time from the investment process. At the same time, data quality and confidence in compliance have improved, along with investor satisfaction. Moving forward, adaptable architecture and multilayer data translation using new technologies can continue the gains that private market funds have made in the first phase of innovation.
Alin Bui is Anduin’s co-founder and Chief Strategy Officer.
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