( generally captured by human and rules systems ). Modern algorithms then use this data to learn to identify fraudulent transactions .
Through supervised learning ( identifying transactions that resemble previously labelled transactions ), anomaly detection ( identifying transactions that diverge from normal transactions , for example a larger amount to an unusual destination ) and sequence modelling ( predicting the next suggested transactions and identifying the real operations that diverge from the predicted ones ) banks or firms like Linedata are better able to spot financial fraud and money laundering .
Marco Santos : Imagine a digital detective , always on the lookout for suspicious activity . In a nutshell , this is how AI algorithms are being applied to detect and prevent financial fraud .
The underlying basis of these AI algorithms is a significant amount of historical data , including information about what constitutes a fraudulent transaction versus a normal transaction . From there , organisations can use both supervised and autonomous training techniques to guide the algorithms to discern between anomalous and nonanomalous transactions .
“Real-time antifraud systems use behaviour analytics to determine whether to block transactions for fraud ”
DR SCOTT ZOLDI , CHIEF ANALYTICS OFFICER FICO
For example , if the algorithm automatically flags a legitimate transaction as fraudulent , AI teams can step in with data annotation techniques that redirect the algorithm and prevent the same mistake from happening again in a real-world scenario .
AI-powered fraud detection algorithms can analyse vast amounts of transaction data in real-time at a scale that ’ s unattainable by humans . The real-time nature of these systems also allows organisations to prevent loss by intercepting anomalous transactions before they ’ re settled .
This scalable , automatic approach also makes it easier for financial organisations to stay in compliance with relevant anti-money laundering ( AML ) and anti-terrorist financing regulations
192 November 2024