How one lender achieved a 5 month payback

See how a single attacker leveraged fraud ring tactics to compromise over 50 accounts in under 30 days—and how to detect and stop solo account takeover attacks at scale.

Key insights:

• Lone fraudsters can replicate fraud ring tactics using playbooks, tools and stolen credentials
• A single device can target dozens of accounts rapidly with consistent, repeatable actions
• Behavioral, device and location signals help distinguish imposters from legitimate users
• Stopping high-risk devices early prevents attacks from scaling across multiple accounts

Uncover a coordinated account takeover fraud ring in action and learn how it was stopped using behavioral, device and network intelligence.

Key insights:

• Fraud rings operate with structured roles to efficiently execute account takeover attacks
• Compromised accounts can be connected across devices, revealing coordinated activity
• Behavioral, device and network intelligence expose fraud signals traditional tools miss
• Stopping ATO requires identifying and dismantling the full fraud ring, not just individual logins

Bayesian Optimization offers a more efficient way to tune machine learning models at scale. This white paper presents a controlled comparison with Grid Search using production credit-risk data to measure runtime and model performance.

• Up to 80× faster hyperparameter tuning
• No loss in CV or holdout AUC
• Evidence from LightGBM credit-risk models
• Guidance for governed ML environments

This tip sheet explores where fraud can enter the customer lifecycle and how predictive fraud risk scores help distinguish fraud from true credit risk and help you:

• Reduce first-payment default and bust-out fraud
• Detect synthetic identities earlier
• Improve prescreen and campaign ROI
• Strengthen account management decisions