Card-Not-Present fraud prevention software built on Machine Learning technologies enables more accurate fraud detectionContact us
Card-not-present fraud (CNP) is a type of credit card scam in which the customer does not physically present the card to the merchant during a fraudulent transaction. Most сard-not-present fraud cases occur with transactions that are conducted online.
Hacking, skimming, phishing, or purchasing data on the dark web are the main methods of CNP fraud.
How to prevent card-not-present fraud? Financial institutions and banks should implement modern Machine Learning based card fraud detection tools. Such solutions enable higher accuracy in transaction risk detection than rule-based systems.
The number of payment cards globally. An average American has more than three credit cards
Of all credit card fraud cases are card-not-present fraud
Forecasted losses from CNP online payment fraud by 2022
Verify customer information, card numbers, CVVs, any history of fraud associated with that data, and other factors to rate the likelihood of a fraudulent order.
Order approval rates, chargeback rates, total decline rates etc.
Monitors customer transactions on a daily basis or in real time for risk. ML algorithms are used to find very subtle and usually hidden events and correlations in user behavior that may signal fraud
Enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise. Eliminates the delay between spotting the problem and resolving it.
Evolves automatically based on the insights acquired from newer incidents. As the database grows larger, the number of patterns identified also increases, leading to more accurate insights.
Compared to rule-based solutions, machine learning tools have higher precision and return more relevant results as they consider multiple additional factors.
False declines or false positives happen when a system identifies a legitimate transaction as suspicious and wrongly cancels it. ML algorithms significantly reduce this factor.
ML-driven systems filter out 99.9% of normal patterns, leaving only 0.1% of events for experts to verify.
Graphical summaries, powerful dashboards, and real-time alerts are built into the system for better transaction monitoring and fraud prevention.