Traditional Fraud Prevention Falls Short in Age of AI
Paymnts.com reported recent data (from West Monroe Partners) that found 77 percent of banks are already putting AI solutions to use in some way. Count Chase as one of the forefront adopters of Artificial Intelligence solutions and technology.
Chase is embracing AI and ML to help customers conduct business while preventing fraudsters from making off with data or financial assets. Andrew Sloper, head of digital identity and authentication at Chase, recently spoke to PYMNTS about how these solutions allow the bank to deliver seamless and secure user experiences while enabling a preventative approach to fraud.
“What we aim to do as a bank is keep in mind that our main focus is to protect customers, their data and their money and deliver a digital experience,” Sloper said.
Machine Learning (ML) is used to gather data and pinpoint fraud patterns, allowing a level of vigilance that is unattainable with “mere human” management.
PaymentsJournal.com agrees that current fraud management is not up to the task of preventing fraud now that AI is available and more and more accessible. Furthermore, the personnel being deployed looking for fraud could be accomplishing something more constructive to the bank’s business longevity:
It is not too much of a stretch to imagine most of the fraud risk strategy process becoming automated. Instead of the expanding teams of today performing the same manual task continually, those same staff members could be used to spot enhancements in customer insight.
PaymentsJournal.com suggests that organizations should ask themselves:
- Are technology solutions/providers allowing you to scale with ease or creating more bottlenecks?
Check payments make for a strong complementary play to ML when you consider the ability to use data recognition, field validation, and image analysis on check images. By scoring the various fields on a check image captured from anywhere within the omnichannel of a financial institution, ML can be applied to these scores for unsupervised learning. The result; ML may be able to uncover various use cases illustrating checks and other paper-originated negotiable items which have common attributes — something a human could never uncover on their own!
This blog contains forward-looking statements. For more information, click here.