Data Action Layer February 8, 2021
The explosive popularity of alternatives assets, including private equity, art, precious metals, venture capital, M&A, and real estate, provides a seemingly unlimited set of opportunities for investors to diversify holdings while boosting returns. Most notably, private equity’s net asset value has grown more than sevenfold since 2002, twice as fast as global public equities. But alternatives come with their own set of challenges.
Whereas public company data is readily available, the data on alternatives assets can be sporadic and highly variable. This means diversification can expose a portfolio to the unintended result of even more risk.
Or with the right tools, a significant competitive edge.
Alternatives assets are notoriously difficult to use for several reasons:
1) Multiple formats. The data comes in many forms from many sources: capital calls, distribution notices, quarterly reports, capital account statements, investment schedules, portfolio company data, K-1s, and more.
2) Unstructured data. Most alternatives asset data is in e-mail and documents, making data collection efforts extremely labor-intensive and expensive. Total reliance on manual data entry by operations analysts is not an efficient use of your resources. Neither is outsourcing the data processing to third parties that gain access to your most valuable data.
3) Timely access to data. Timing is tricky. Alternatives are events-driven; a specific activity such as a call, a distribution, or a new exposure report can have an immediate, significant impact.
According to SimCorp’s 2019 North American InvestOps Report, a survey of the top challenges investment firms face supporting alternatives investments include:
That’s why data extraction and process automation technologies are game-changers for financial firms using alternatives and private market data.
When investing in this type of technology, it’s important to consider which tasks in Investment Operations can be automated and which ones should not.
Machine learning (ML) and robotic process automation (RPA) combined can extract data from some documents to be used in a workflow. Still, they won’t hold up well when alternatives data needs to be 100% accurate and auditable.
That’s where human review (HITL) becomes an essential component of processing. HITL creates a feedback and control loop that improves how the software copes with exceptions over time.
Investment professionals need to make quick and confident decisions with alternatives asset data, and with Alkymi Data Inbox, investors can:
In short, Alkymi can help put the final brushstrokes on an alternatives-asset balanced portfolio with less risk and significantly more reward.
Fine-tuning is not the only way to get relevant, domain-specific responses out of an LLM. Alkymi’s team of expert data scientists explain an alternate route.
Find out which type of automated document processing solution is right for you: data extraction, an IDP, or a complete business system for unstructured data.
We’re partnering with Portfolio BI, a provider of portfolio analytics and reporting solutions, to bring structured and unstructured data sources together.