Fighting Fraud with a Model of Models explains how utilizing human expertise in combination with artificial intelligence (AI) and machine learning (ML) technologies can significantly increase the accuracy of fraud prevention services. Fighting Fraud with a Model of Models explores the theoretical approach behind Nets Fraud Ensemble, an AI-powered anti-fraud engine developed in collaboration with KPMG, which can reduce fraudulent transactions by up to 40% on top of existing AI fraud prevention measures, for the benefits of banks, merchants and cardholders, as well as society in general.
The ‘brain’ of Nets Fraud Ensemble consists of multiple models working together to analyse each individual transaction within ten milliseconds – the time frame in which a transaction can be safely blocked. The solution learns from the results of its analysis and adjusts accordingly, meaning the longer that it is operational the more fraudulent transactions are blocked, and the fewer false positives are granted.