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they leave the bank
Download white paperDiscover the fastest-growing area of banking fraud. It poses
challenges for banks because it usually involves run-of-the-mill
deceptions and confidence tricks
Download case studyWithin seven months, NG|Screener had analyzed more than five
million transactions yet blocked 10 times fewer payments than
its former system and achieved a better fraud detection rate.
Real-time API scoring of all customer and employee transactions across the payment channels, SWIFT and other networks.
User behavior analytics and machine learning detect new cyber and internal fraud threats. You stay on top of banking fraud schemes protecting your customers.
Machine learning algorithms keep false positives to a minimum ensuring the frictionless customer experience.
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.
Beating Banking Fraud with NetGuardians’ Machine Learning Risk Platform
Investigate hits
Unusual amount
Unusual client browser and device location
Our AI models do all the heavy lifting. Built to detect banking fraud, our predefined risk models do not focus on an ever-changing list of potential fraud scenarios, but on behaviors of employees and customers to spot unusual transactions.
The more we use NetGuardians the more we realize what we can do with it
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 to overcome overfitting in machine learning based fraud mitigation for banks?
Global risk score
Transaction information
High risk variables
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
Stop eBanking/mBanking session transactions redirected by malwares, hijacked by remote hackers, taken over by identity theft, etc.
Stop fraudulent transactions due to social engineering or scams such as invoice scam, CEO fraud, etc. Meet PSD2 and SWIFT CSP requirements.
Prevent internal banking fraud due to employee collusion, internal-external collusion, internal controls bypass by client advisors, etc.
Financial fraud and cyber-crime: New horizons in financial crime prevention (Asia)
