FIS Turning to Machine Learning for Private-Equity Solutions

FIS is turning to emerging technologies, particularly robotic process automation (RPA) and machine learning, to boost its capabilities in the private-equity space.

Tony Chung, general manager and global head of private equity at FIS, says the goal is to further automate and streamline workflows for private-equity firms.

One of the challenges for private-equity firms is that a lot of the data collection is manual, so there is a risk for human error and operational risk when employees who gather the data leave.

Tony Chung, FIS

“We are heavily focused on analytics and emerging technology, including looking at opportunities around RPA and machine learning to streamline a lot of processes,” Chung says. “We can use machine learning to look for patterns in the data, which is paramount for private equity since it’s not a very transparent space.”

He adds a lot of the projects FIS is looking at around emerging technology and streamlining workflows have either been recently released or in the process of moving to production.

Chung notes that much of the work for pulling together private-equity data is still largely manual, which can be a problem when it comes time to determine the performance of an investment or risk management. In light of this, FIS released its portfolio data collection and analysis solution called Private Equity Rainmaker, which uses some RPA and machine learning to both ingest information and analyze that information to alert managers to patterns in the data. Clients can access Rainmaker through a web portal.

“What it does is automates the collection of data and then it normalizes this data to better track the performance of investments to other benchmarks. This then provides decision support for the front-office, say for profit-and-loss, so if a fund wants to exit an investment, they have the information they need,” Chung says. “One of the challenges for private-equity firms is that a lot of the data collection is manual, so there is a risk for human error and operational risk when employees who gather the data leave.”

He adds what normally happens is an employee would be tasked to call up a company to get financial data and then encode it to their systems. Rainmaker can take in the financial information provided by companies in most formats, usually through an Excel spreadsheet or a PDF, and carve out those data points to fit into FIS’s database and format it for necessary reports.