Fraudsters never seem to rest; in fact, 77% of anti-fraud professionals surveyed by the Association of Certified Fraud Examiners (ACFE) have reported an increase in fraud since February 2020—and they expect it to continue.1 For companies that have put effective fraud prevention and detection tools in place, that means that the volume of alerts often increases organically as well. With every alert, due diligence demands that an investigation needs to take place. Not only do these investigations consume your organization’s precious resources, but they can also cause friction with customers who tire of being contacted about the veracity of the transactions in question.
Machine learning is a great tool, but at some point, a financial institution may find itself out of step with where it needs to be on the cost vs risk continuum when it comes to managing its alerts. Tough decisions must be made regarding the assumption of more risk—by reducing the level and number of alerts—or taking on more costs by adding more investigators to the team. The equation can be further complicated when an organization adds business volume but is unsure how much their investigative resources need to be scaled to maintain an appropriate level of response.
This push and pull of cost vs. risk is a constant balancing act that all organizations must work through. However, for customers who rely on Bottomline Technologies’ proprietary fraud detection solution, Secure Payments, there are additional ways to manage these challenges and enable confident, data-driven decision-making. The real-world use cases below demonstrate the ability to strike the right balance with incredible precision using Bottomline’s additional analytics services, BTune and F.A.S.T. (Fraud Analytics Simulation Tool).
