Credit Card Fraud Prevention software built on Machine Learning technologies enables more accurate fraud detectionContact us
The number of payment cards globally. An average American has more than three credit cards
Global annual losses from fraud generated by card-based payment systems
Global penalties for AML failures in 2019 with an average of $143.5 million per fine
Financial institutions and banks have to prevent financial losses and fraud at the earliest stage. Card fraud is the no. 1 cause of identity theft.
Almost 30 billion dollars were lost worldwide to card fraud. Modern credit card fraud prevention is expensive. Digital ID checks cost around $2 per document, companies spend millions on KYC and AML, and still, the number of fraudulent transactions is growing. Banks and financial institutions have been relying on passive measures to counteract fraud based on past breaches or fraud behaviour history, and only some have invested in pro-active or predictive fraud prevention.
Modern Machine Learning based Credit Card Fraud detection tools enable higher accuracy in transaction risk detection than rule-based systems. AI & ML technologies can consider many more data points, including the tiniest details of behavior patterns associated with a particular account. Less manual work for additional verification. Fewer false declines.
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.