Asset allocators, whether they invest in hedge funds, private equity, or other managed accounts and comingled funds, have to process a massive number of documents as a byproduct of those investments. Portfolios with 100 private investments receive an average of 4,000 documents annually from fund managers, including transaction and capital call notices, valuation statements and financial statements. These documents need to be immediately categorized and sorted, with their information captured and made accessible as needed. Manually processing these documents—a time-consuming endeavor that carries substantial risk for errors—is a major roadblock to producing a consolidated view of portfolio holdings.
A document management system that leverages new technologies like natural language processing (NLP) and machine learning can improve the process of capturing and categorizing documents, while robotic process automation (RPA) can be used to create digital “workers” that can be programmed to log into portals and scrape documents for critical information. But many of the generic document management solutions don’t integrate with the portfolio aggregation, operations support and accounting processes and systems required for investment management, so asset allocators need to make sure they choose a solution that they can customize to integrate with the functions they need.