Beating Internal Fraud
Banks still largely rely on manual controls, human processing, sampling, and static rules to detect internal fraud. The problem with this is threefold – alerts are raised after the event, sampling is not failsafe, human processing takes time and is subject to errors and is itself vulnerable to fraud. What is needed is a new technological model that detects and prevents internal fraud.
NetGuardians' enterprise risk platform uses big data and profiling to track behavior and block suspicious transactions. It automatically correlates data from across a bank’s IT systems, channels and labels it, while advanced analytics trigger meaningful alerts in single-view dashboards.
Using behavior analytics and profiling, NetGuardians detects fraudulent patterns and raise alerts for:
Alerts can be investigated or accepted via our dashboard before the transaction is completed. And because machine learning algorithms constantly assimilate new data, the number of false positives is kept to an absolute minimum, reducing the need for large risk management departments and maximizing the use of resources. It is a cost-effective defense in the fight against fraud.