Payment Fraud Detection & Prevention
Immediately know when your ATMs, cards or payments switches are under attack with INETCO Solutions
With adaptive machine learning capabilities and unrestricted access to real-time payment transaction data, financial institutions, retailers, card service providers and acquiring processors can now reduce financial loss and customer friction with precise, real-time transaction risk scoring and independent monitoring of the entire payment transaction journey – across ATM, POS, Mobile and Online channels.
INETCO’s solutions can further help you meet customer security expectations, speed up transaction anomaly detection, and protect your ATM and POS terminals against potential skimming. If preferred, transaction data can also be collected and forwarded from INETCO Insight to your existing fraud management system.
- Extend switching and card processing security – Correlate front-end and back-end transactions to know when a transaction enters a payments switch, but never leaves for authorization – most likely due to advanced malware being used to compromise switch authorization behavior
- Monitor foreign card transactions – Detect when another bank’s cards are counterfeited and repeatedly being used at your ATMs to withdraw cash – before an ATM cash-out happens
- Speed up the detection of ongoing, front-end APT attacks – Quickly identify customer behavioral anomalies, vulnerable terminals, and compromises to the payments switch such as:
- Repeat card usage at the same terminal or across an unlikely geographical area
- High withdrawal velocity or abnormal numbers of high-value transactions
- Multiple cards used in sequence at the same terminal
- Isolate vulnerable terminals – Flag suspected terminals being used to conduct coordinated attacks, or terminals where imposed cash withdrawal limits have been eliminated and hacked
- Spot EMV fall-backs, unexpected stand-ins and newly compromised domestic cards – Take action before they make it onto a hot list or are used at foreign terminals
- Rebuild individual customer models in real-time – Continuously feed adaptive machine learning models and rebuild them every time they receive an event from a customer