The financial industry has been all over artificial intelligence (AI) supporting front-end trading processes, leaving much of the rest of the business in the last century. That won’t be true for much longer if Bikram Singh, founder and CEO of EZOPS, has his way.
I caught up with Singh during the recent kick-off of the SAP Next-Gen Innovation Community for Financial Services at the SAP Leonardo Center in New York City.
“A lot of functions in the middle and back office typically have been neglected in the AI revolution, and we see a tremendous opportunity here to radically transform the landscape,” said Singh. “We are specializing in applying AI and machine learning to address use cases in the back office, including data reconciliation.”
According to Singh, up to 15% of a financial institution’s staff focused on trading are conducting repetitive, mundane tasks to reconcile data. He co-founded EZOPS three years ago to provide cloud-based, AI-fueled services that support these processes that take place after trading is done. Based in the United States with offices in Dublin and India, this fintech is designing a product called ARO to help companies predict and resolve breaks. The software can help institutions streamline operations, reduce risk, and redirect workers to higher-value responsibilities. It’s aimed at top-level asset managers and fund administrators in any financial institution involved with heavy trading volume, including equities, cash, and commodities at banks and insurance firms.
“Our passion is to connect with the C-suite, giving them the satisfaction that at the end of the day, after they have executed trades in the front office, they don’t have to worry about the back office,” said Singh. “Applying AI in the middle and back office has benefits up and down the entire value chain, and this is where the industry is headed.”
Time to trade up pre-2008 systems
Singh began his financial career at the trading desk, where he fell in love with the back-end processes. “I wasn’t a great trader, but loved processes and what happened on the back end, how if I pressed a certain button from the front office, everything fell into place through settlement. I’ve spent my entire career building products and services around that middle- and back-office spectrum.”
AI is tailor-made for a financial industry increasingly squeezed by new regulations on selling products and resultant decreasing revenues. Calculating ROI at a molecular level, meaning costs by trade against worker productivity, has become a must-have. That’s where AI comes in.
“You can’t have a back or middle office set up for pre-2008. Institutions must become more streamlined and nimble,” said Singh. “Banks are realizing even if policies change, certain regulations and cost pressures are here to stay. If they don’t adapt and be one of the first movers, someone else will do it.”
Singh said EZOPS is having serious conversations with several of the world’s largest banks about the company’s AI-based solution, now in proof of concept (POC), which runs machine learning algorithms on a cloud platform, and uses analytics for reporting.
“We are getting over 90% accuracy in predictions from our [proof of concept] in results that are off the charts,” he said. “Because of the intelligence in our algorithms, based on our understanding of the processes, we can deliver results that are immediately promising and translate into cost savings. For banks, we feel like we are their BFFs, providing them with results they can show their board and investors and benchmark against their peers.”
For more on the disruptive effects of AI, see Artificial Intelligence Will Require Very Real Brainpower In Finance.