Traditional anti-fraud models are static. This means they're limited to the data they were trained on. Fraudsters exploit this by constantly developing new strategies, leading to massive financial losses for institutions. Fraud patterns are evolving at a much faster rate than traditional anti-fraud models can detect. The proof is in how infrequently these models are updated – typically only every 9 to 12 months.

WITH FRICTION INCREASING AND FINANCIAL LOSSES MOUNTING, CAN YOU AFFORD TO WAIT THAT LONG?

4KST Anti-Fraud Models are incremental and adaptive. This means they're not constrained by past data because they can update in real-time to new fraud patterns. No matter how fraudsters change their strategies, the 4KST Anti-Fraud Model will identify them.

1. Origination Fraud: Detects fraud at the point of contract origination (e.g., credit/debit card issuance, e-commerce, insurance, current account opening, etc.).

2. Transactional Fraud: Detects fraudulent transactions even under demanding conditions for performance, scalability, and security. This includes models for validating banking transactions, e-commerce, and more.