Real-time API scoring of all customer and employee transactions across the payment channels, SWIFT and other networks.
Reduced fraud losses
User behavior analytics and machine learning detect new cyber and internal fraud threats. You stay on top of banking fraud schemes protecting your customers.
Reduced false positives and improved customer experience
Machine learning algorithms keep false positives to a minimum ensuring the frictionless customer experience.
Designed for banks
NetGuardians’ software is specifically designed to help banks detect and prevent fraud. Plugging directly into core banking systems via pre-defined connectors, it extracts, enriches and analyzes data, spotting and stopping significantly more fraud and reducing investigation time.
Our AI models do all the heavy lifting. Built to detect banking fraud, they monitor all the relevant variables to spot and flag suspicious behaviors attached to any transaction. Alerts can be easily investigated by business users.
The more we use NetGuardians the more we realize what we can do with it
Tauch KempoulVP and Head of Security Operations - Acleda Bank
Within the millions of transactions a bank processes each year there will be a small number of frauds. While traditional AI systems can be taught to spot these frauds, they will become experts only in these known frauds – overfitting. NetGuardians’ managed learning technology doesn’t endlessly learn about any given type of fraud. It prevents AI from continually diving down just a few avenues. This avoids overfitting and makes it smarter and more dynamic, able to spot new types of fraud.
How artificial intelligence helps banks to fight fraud
You don’t need to be a data scientist to make sense of AI algorithms. Easily understand why AI raises an alert with the full business context and run powerful forensics using intuitive investigation tools.
Customer success story – Commercial Bank of Ethiopia
Daniel Hailu TeshomeHead Director - Commercial Bank of Ethiopia
NetGuardians’ fraud-mitigation software is proven to offer unrivalled fraud detection. When run over a bank’s historic data, it found more fraud cases with fewer false-positive alerts than traditional fraud-mitigation processes. It cuts risk, cuts investigation time, and cuts fraud losses.
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