AI for Finance in Banking
AI and ML are already transforming the finance function across the banking industry and helping deliver better employee experiences, improve operational efficiencies, and provide insights for faster data-driven decision-making. To modernize finance, teams need to eliminate manual, repetitive tasks to free up time for strategic work.
“Applying AI and ML is equally essential to the future of finance,” says Sayan Chakraborty, co-president and leader of Workday’s product and technology organization. “Finance teams can get help managing risk and eliminating inefficiencies by reducing what used to take months or weeks down to just hours or minutes.”
In Workday and IDC’s recent enterprise software survey, banking leaders worldwide selected the top AI and ML capability they’re using or considering using in their organization.
36% automation (auto-skip approvals, self reconciling accounts)
29% anomaly detection (journal entries, expense reports, plans, outlier reports)
33% recommendations (customer payment matching, spend category recommendations, supplier invoice automation, and intelligent demand forecasting)
Interestingly, only 1.6% were not considering any of those options, showing the real need for the help AI and ML can bring.
Traditionally, day-to-day finance functions—from detecting anomalies to identifying fraud to predicting outcomes—were done manually. Now, as finance faces increased expectations to work efficiently and provide strategic insight, organizations must adopt AI technologies that offer greater automation, integrity, accuracy, scenario planning, and data-driven predictions.
A big need for accounting departments is to reduce incorrect numbers or inaccuracies through anomaly detection, which is challenging with the sheer amount of data, invoices, and reports. One way to address this through ML is journal insights. ML helps surface erroneous journal lines for controllers—dramatically reducing the time and overhead spent by finance teams to close the books.
“Workday Journal Insights means one less thing for our end users to check off their list at the end of the month. They can correct issues and can fix them throughout the month. It makes it a continuous process,” said an enterprise resource planning business analyst at IMC Financial Markets.
Journal Insights uses machine learning to detect anomalies in accounting entries by comparing them to other entries for similar transactions. Because entries are flagged in real time, a user can correct potential reconciliation issues and fix them throughout the month in a continuous process—avoiding the end-of-the-month bottleneck. This enables accounting teams to spend more time on analysis and tackle more strategic initiatives.
For financial planning and analysis (FP&A), accurate forecasts are essential. With ML, FP&A teams can leverage historical data to further drive predictive demand forecasts. With real-time analysis, AI can help incorporate other data sets to drive greater precision. This opens the door to a new kind of planning that continuously learns from data and adapts to a changing world.
This investment can help banking and capital markets executives run sophisticated scenario planning while allowing more time for strategic analysis. It can also allow them to easily track details from various subledgers—a process that’s nearly impossible when using legacy systems—to quickly analyze critical metrics and create more nuanced risk analyses.
“It’s about having a platform that’s going to give you the ability to react, to be agile, and to be resilient to all those changes in the industry,” says Viren Patel, strategic industry advisor for financial services at Workday.