AI algorithms are being used to develop sophisticated anti-fraud systems that can analyse transaction patterns in real-time to detect anomalies and prevent financial losses . How do these algorithms work ?
new products or services to roll out in advance of shifting customer demands .
Prashant Jajodia : These algorithms typically employ machine learning techniques , specifically supervised learning methods , to analyse historical transactions and identify patterns associated with fraudulent activities .
They learn from labelled datasets containing both legitimate and fraudulent transactions , enabling them to classify subsequent transactions based on their similarity to these learned patterns . As new data becomes available , the algorithms retrain themselves to adapt to evolving fraud patterns .
Jamil Jiva : The most efficient AI models leverage reconciliated 360 ° views of clients based on a company ’ s front , middle and back-office information . It collects KYC information , historical operations and transactions , and external news and sanction data to understand the transaction profile of each client and identify diverging patterns .
Learning algorithms are fed with examples of these client profiles as well as a series of transactions which for some profiles are tagged as fraudulent
190 November 2024