UBS Deploys Predictive AI To Improve Expense Management

Swiss financial services conglomerate UBS is taking steps to improve its own back-office finance functions, working with PredictX to do so.

The companies announced news on Wednesday (Jan. 17) that UBS will integrate PredictX artificial intelligence and predictive analytics to improve its travel and expense management. According to UBS, the financial institution (FI) is working to develop more accurate corporate travel budgets and forecasts that recognize the nuances of seasonality.

UBS said it must also ensure compliance with supplier agreements and produce more accurate spend reports.

“PredictX’s platform will be at the heart of providing this unprecedented intelligence,” UBS said in its announcement.

PredictX collects travel data across travel management companies, vendors, commercial cards and expense management platforms to underwrite its predictive analytics, which are produced against a company’s actual travel spend.

“For us, accurate and transparent data analytics is mission critical, especially in a high spend category such as travel,” said UBS Global Travel Lead Mark Cuschieri in a statement. “Previously, it was impossible for our category managers to make proactive, vital business decisions on future spending. Typically, we were receiving static reports on trip spend eight weeks post-event. Slow data consolidation and single layer reporting just couldn’t provide the insight needed — and certainly not in a way that would support our new global travel strategy and business requirements.”

“With PredictX, we can be far more precise with our preferred suppliers when negotiating new rates,” he added.

“Machine learning and predictive analytics, in contrast, have the potential to uncover insights that would otherwise remain invisible, immeasurably improving the accuracy of forecasting and real-time decision-making,” added Keesup Choe, CEO of PredictX. “In business functions that inherently generate and rely on enormous amounts of data — and there are few that do not fit this description — this degree of analysis has now become so prevalent that it is practically an expectation.”