I have a credit card fraud detection ML program I’ve built. I’ve applied bagging to SMOTE which decreased accuracy slightly… the difference was less than a percentage point but I’m concerned that this indicates I’ve messed with the overfitting. How can I check if it’s all okay?
Kochava Collective offers its Global Fraud Blacklist as part of its Mobile Ad Fraud Detection and Prevention program—a suite of software that identifies and prevents mobile ad fraud through its blacklist, custom thresholds capability, and web traffic verification system. Kochava tracks billions of transactions daily and its blacklist is correspondingly thorough.
DataX’s Fraud Prevention suite provides insight needed for businesses to lower consumer acquisition costs and increase revenue opportunities.
GBG Instinct Hub helps users combat fraud by identifying high-risk individuals and financial transaction before they can have an impact on the organization or business.
Graydon Corporate Fraud Intelligence provides quality corporate B2B fraud and financial crime detection services
ID Network Attributes for Fraud deconstructs ID Score, tapping into the unique blend of cross-industry consumer behaviors in the ID Network to provide meaningful insights.