New research from AutoRek finds U.S. financial firms under pressure to modernize data management strategies or risk falling behind as data volumes surge
Nearly four in five (79%) financial firms are struggling to manage their reconciliation data volumes, and more than half (57%) still rely on spreadsheets, in-house systems, or outdated legacy software. That’s according to new research from AutoRek, a leading software provider to companies in the global financial services sector. As data complexity grows, outdated processes are creating inefficiencies and exposing firms to significant operational risks.
The report commissioned by AutoRek sought the views of 250 senior managers, or above, in asset management, capital market, hedge fund, and fund administration organizations across the country. It shows that firms are expecting a 39% increase in daily volumes over the next two years, putting additional strain on existing systems which are ill-equipped to manage such growth.
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“While asset management firms have invested in modernizing their front-end business, very often back-office operational processes have been neglected. Firms are relying on outdated systems and processes, which not only create inefficiencies but also expose them to greater risk,” said Jack Niven, VP of North America, AutoRek. “With transactional volumes only set to increase and regulatory demands tightening, it is imperative for firms to invest in modern data infrastructure. Failure to do so will leave them at a competitive disadvantage in an increasingly data-driven and regulatory-intensive environment.”
As asset management and capital markets firms look to move beyond legacy systems and manual processes, the report highlights how automation offers a clear solution by providing firms with the tools they need to reduce costs, improve efficiency, increase control, and meet increasingly complex regulatory demands. More than one third of firms (36%) are currently focusing automation plans on their reconciliation and transaction matching processes.
Integrating advanced technologies like artificial intelligence and machine learning will offer huge benefits including increased productivity (36%); increased operational efficiency (36%); a reduction in human errors (29%) and the ability to handle increasing data volumes (28%).
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