Case Study: Retail Bank achieves 83% fewer false positives and 93% faster fraud investigations with NetGuardians' machine-learning risk platform

Retail Bank - Customer Case Study

Retail Bank faced an underperforming rule-based fraud control environment that could analyze only 32% of its 10M+ annual payment transactions, producing many false positives and costly manual investigations. To address this, the bank evaluated NetGuardians’ machine learning-based risk platform across a 12-month dataset to improve detection coverage and reduce operational burden.

NetGuardians implemented a machine learning platform that analyzed 100% of unique payments and unlocked new fraud types from Big Data, delivering an 83% reduction in false positives, a 93% cut in fraud investigation time, and a 118% outcome where all previously detected frauds were still caught plus additional cases discovered. The NetGuardians deployment reduced operational costs, improved customer experience by cutting callback volume, and strengthened fraud protection.