Fraud detection systems become obsolete within weeks as fraudsters adapt faster than detection rules can be updated

Fraud teams deploy ML models and rulesets to catch fraudsters, but these defenses become ineffective within weeks as bad actors evolve their tactics to bypass detection logic. Companies lack the data science resources to continuously retrain models and update rules in real-time, forcing them to choose between hiring expensive ML teams or accepting rising fraud losses. Traditional fraud platforms like Sift and Sardine require labeled training data that most businesses don't have, making them slow to deploy and even slower to adapt.