Most banks rely on teams of human analysts to examine transactions for potential financial crime, but these teams encounter numerous issues. Forty-five per cent of banks say their investigations take too long to complete, and 40 per cent say the investigations result in a high number of false positives, which occur when legitimate transactions that have been mistakenly flagged as fraudulent. Banks can even have false positive rates of more than 90 per cent, resulting in unpleasant experiences for customers as they are forced to resubmit their transactions.
The pros of AI
Financial institutions are exploring many avenues to overcome these stumbling blocks, but few are as promising as artificial intelligence (AI) and machine learning (ML). Here are the fraud-fighting benefits of these systems, as well as the challenges that many banks face in implementing them.
Detection systems driven by AI offer a number of fraud prevention benefits, as they can analyse transactions holistically, comparing each data point within a transaction to every other data point in fractions of a second.
These systems can also compare each transaction against every other transaction banks have ever processed to determine its likelihood of being fraudulent based on variables a human analyst might never notice, such as attempts to log in to the same account with different usernames and passwords over the course of several months or uncharacteristically large transactions.
Banks are deploying AI-based systems in record numbers, with more than $217 billion spent on AI applications for middle-office use cases like fraud prevention and risk assessment.
These investments are paying off, according to fraud prevention specialists, as 80 per cent of experts say AI reduces payments fraud and 63.6 per cent of financial institutions cite AI as a valuable tool for halting fraud before it succeeds. These systems are commonplace at large banks that have more than $100 billion in assets — 72.7 per cent of which leverage AI — but only 5.5 per cent of all financial institutions reportedly have an AI-based system in place.
The most obvious explanation for this gap is AI systems' expense, but there are a number of other concerns that keep banks from jumping aboard the AI bandwagon. AI systems often do not operate in real time, with 45.6 per cent of fraud specialists citing this as a concern — a significant obstacle for pay-ments that need to be processed instantly. A lack of transparency is a problem as well, according to 42.8 per cent of specialists. A human analyst could definitely provide justification for rejection of any given transaction, as opposed to many AI systems, whose reasonings may much more nebulous.
Some of these concerns can be addressed by ML, a more advanced form of AI. ML systems take past transactions into account and apply these rules to future analyses to detect financial crime, making them gradually more adept at fighting fraud over time. Financial crime attempts against banks will likely never cease completely, but the addition of AI and ML to the’ fraud-fighting arsenal of financial institutions could go a long way toward making these attempts less likely to succeed.
This article first appeared in the Preventing Financial Crimes Playbook in August by PYMNTS.com, the business-to-business platform for the payments industry