Transaction Fraud Prevent fraud and minimize losses
Monitor transaction behavior to detect fraudulent patterns across all channels and payment methods, including chargebacks, card-not-present fraud, scams, and account testing schemes.
Detection of unusual transaction patterns
Real-time processing of transactions
Automated decisioning for fewer investigations
Fight fraud and improve AML compliance with a holistic view
By combining fraud and AML (FRAML) efforts, fraud professionals and AML compliance officers can combat crime with a consolidated system.
- Minimal migration and integration effort (<4 months) due to the most modern tech stack, offering unlimited scalability in any cloud environment
- Better fraud and AML detection due to a holistic customer view from Customer Screening, Customer Risk Rating, AML Transaction Monitoring, Fraud Detection, and Payment Screening
- Reduced investigations with automatic AI decisioning utilizing a smart and self-learning system to decrease manual reviews. Our intelligent AI models learn from customer context and past operator decisions.
Reduce customer friction through AI-based false-positive reduction
Blocking legitimate transactions can result in friction during the customer journey. Hawk uses traditional rules augmented with AI to reduce false positives, leading to fewer upset customers. Our AI models are constantly retrained with operator feedback and cross-institutional learnings, understanding normal customer behavior and effectively identifying falsely alerted cases.
AI: the most effective way to search for known fraud patterns
AI powers rule-independent detection of known fraud typologies to deliver well-explained, comprehensive fraud alerts, immediately stopping any type of suspicious transaction.
Our Pattern Library contains a wide variety of pre-trained, off-the-shelf AI models for complex fraud typologies. We make new patterns available to all customers immediately, without additional costs.
Behavioral analytics detects deviations from expected behavior
Hawk models learn customer transaction patterns, allowing for the detection of fraud without the need for fixed thresholds.
- Catch deviations from normal customer behavior in the context of similar customers in their peer group
- Detect and prevent new, emerging fraud patterns
