Digital transformation could solve data fragmentation in private markets
Couldn’t attend Transform 2022? Discover all the summit sessions now in our on-demand library! Look here.
Private markets have an outsized impact on global capitalism. They transfer billions of dollars to funds and investments each year, often channeling them into high-tech development companies. Yet the funds themselves are underinvested in technology, investing only a third to half of what public financial institutions pledge to innovate as a percentage of their revenue. The resulting hangover from legacy methods has hampered investor experience and data management since the inception of most funds. This bottleneck – at the very point where capital arrives – has confused both investors and fund managers and has persisted throughout the fund life cycle.
Pain (symptom) and underlying causes (data fragmentation)
Private markets, a driver of investment in technology innovation, have long awaited the digital transformation of their critical capital raising and fund management activities. Transaction execution and compliance also depend on these processes. Virtually all participants – from investors (limited partners or LPs) to fund managers (general partners or GPs) and their attorneys and fund administrators – have felt the inefficiency of archaic paperwork when onboarding investors. Relying on PDF forms, Excel spreadsheets and manual processes has become more problematic recently, due to a shortage of talent that coincides with the need to adapt to a larger LP market including retail investors .
After COVID-19, more funds have accelerated their adoption of workflow automation and this is a major step forward, but not the complete solution. Indeed, a major obstacle to optimizing bottom formation and relationships with LPs is the long-standing sediment layers of messy data. on which the industry revolves. Investors, regulators, each fund or family of funds, and different holding companies structure and view their data differently.
Meeting this challenge is a complex exercise in strategic architecture choice and data “translation”.
Modernizing private markets, starting with fund formation
Process automation can radically improve the investor experience, reduce their data entry errors, meet compliance requirements, and manage the LP lifecycle. The workflow to gather the required information replaces the onerous and friction-spoiled sequences to qualify and onboard investors. In addition, it guides investors in entering their information correctly and performs data integrity checks. Funds can reduce onboarding time and friction, accelerate fund formation, and deliver the red carpet experience their investors expect. Today, as private equity investment has slowed, this is compelling for fund managers.
As is the case 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.
Facing data disparity head-on or halfway?
Once fund operations are up and running, it’s obvious that each fund has its own data model and holding companies have their own structures for reporting results. A standardized industry-wide data protocol would be ideal for private markets, but it is also elusive and will require agreement among many players. This means that it is up to practitioners and software vendors to adopt tools and methods to standardize data and work around fragmented and disparate data structures. Building this kind of platform requires careful architectural trade-offs between being prescriptive (“our way, or no way”) and more adaptive (“your way, if necessary”).
A workflow solution must balance a standardized, defined approach with the ability to customize and match specific fund practices. Larger backgrounds, in particular, tend to require more customization. Keep in mind that a solution will need to adapt to changing compliance requirements; it is imperative to verify that each investor is qualified and meets the requirements of the SEC and to maintain the fund in compliance with its fiduciary obligations to investors.
New technologies 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, especially if they incorporate domain-specific business logic. Cutting-edge technologies are likely to be integrated into private markets as they embrace digital transformation.
- blockchain could end up serving as an “industry ledger” for transactions in private markets, in the future. It is also likely to be useful for both KYC and AML, reducing unnecessary data replication, making it easier to track financial transactions and helping to advance clear and uniform due diligence requirements. . There are already experiments with blockchain for securities transactions. For blockchain to play a major role in private markets, funds need to 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 shortcomings 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 basically bot programs that can automate routine tasks that run on outdated legacy systems. In funds, these essential processes cannot easily be removed 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 more important tasks. Ultimately, RPA bots that are trained in the private vertical market can help offload some aspects of the GP/LP relationship, including batch routing transactional documents and collecting monthly reports.
- AI and ML can further unleash the power of RPAs by injecting smarter analysis and understanding into the picture. AI can make judgments and issue orders to workaholic bots, amplifying their impact and adding use cases to handle more complex scenarios. AI should excel at analyzing and filtering large volumes of data at lightning speed, as long as the data has been collected. The classic AI problem is always how to make sure the data is ready and requires extensive data collection and rigorous human training. These daunting prerequisites can often be overlooked when AI systems are deployed within organizations. With sufficient access to data from across the industry, AI-based systems are expected to boost compliance, due diligence and KYC/AML from the back office, and provide powerful momentum to seek out transaction opportunities from the front office.
- Low code and no code (LCNC) enable platform updates and customization to match fund-specific processes, without relying on software developers. Today’s 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 challenge of data standardization 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-built software modules. With limited or no programmer resources, business or IT specialists can launch basic standalone applications to process investor data and documentation on the backend. This comes with the caveat that codeless programs would be less portable or scalable; have difficulty with extreme cases; and be risky if you interact directly with external customers. With 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 requirements.
By taking the first step in digital transformation – workflow automation – private market funds are fundamentally improving the way they operate, removing friction and wasted time from the investment process. At the same time, data quality and confidence in compliance have improved, as has investor satisfaction. Going forward, adaptable architecture and multi-layered data translation using new technologies can continue the gains that private market funds made in the first phase of innovation.
Alin Bui is Co-Founder and Chief Strategy Officer at Anduin.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including data technicians, can share data insights and innovations.
If you want to learn more about cutting-edge insights and up-to-date information, best practices, and the future of data and data technology, join us at DataDecisionMakers.
You might even consider writing your own article!
Learn more about DataDecisionMakers