We’re different: We don’t find fraud by focusing on the fraudsters. We find it by really getting to know the habits and behavior of a bank’s customers and staff so we can stop financial transactions that are out of character. All fraudulent transaction are out-of-character transactions in some way or another.
While fraudsters constantly change their behavior to avoid detection, real customers form habits. By learning these habits and building up customer and staff profiles we can quickly spot suspicious activity. And stop it.
A bank will see many card frauds every year (which tend to be lots of small amounts) but very few payment frauds (which tend to be rare but large in size). So while the standard machine learning approach works very well on card fraud (balanced datasets) it is not appropriate when it comes to stopping payment fraud (completely unbalanced datasets). This is where the proprietary managed learning approach from NetGuardians comes into play…
How to overcome overfitting in machine learning based fraud mitigation for banks?
How artificial intelligence helps banks to fight fraud?
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Account takeover via social engineering - Fraudulent transaction blocked
Wealth manager scammed - 1 million CHF blocked
Account takeover via social engineering - A total of 50'000 CHF blocked