Financial crime and other forms of digital fraud are a pressing concern for banks, credit unions and other FIs, with fraudsters stealing $2.8 billion from bank accounts in 2018. Banks are devoting time, money and effort to prevent this type of fraud, which stopped $22.3 billion in fraudulent transactions during the same year. They could head off even more, however, by reexamining their often inefficient and intrusive fraud prevention procedures.
Most banks rely on teams of human analysts to examine transactions for potential financial crime, but these teams encounter a host of issues. Forty-five percent of banks say their investigations take too long to complete, and 40 percent say they result in a high number of false positives, or legitimate transactions that have been mistakenly flagged as fraudulent. Banks can even have false positive rates of more than 90 percent, resulting in unpleasant experiences for customers as they are forced to resubmit their transactions.
FIs are exploring many avenues to overcome these stumbling blocks, but few are as promising as artificial intelligence (AI) and machine learning (ML). The following Deep Dive explores the fraud-fighting benefits of these systems, as well as the challenges that many banks face in implementing them.
The pros — and cons — of artificial intelligence