Credit card fraud detection

Challenge

Credit card fraud is a wide-ranging term for theft and fraud using or involving a payment card. It is about obtaining services/goods or money by unethical means – a growing problem all over the world these days. Although such cases are limited to a small percentage of all card transactions, they have resulted in huge financial losses so far. Thus, it is important that credit card companies are able to recognize fraudulent credit card transactions in order to protect their clients from such incidents. However, the task is not trivial – credit card frauds may not differ significantly from normal transactions. Also, more and more cheating techniques are being developed with a view to make a quick a profit.

Solution - credit card fraud protection with AI

The problem of credit card fraud detection can be tackled with the help of the AI module of the Comarch Fraud Protection software. There are two key features of our solution: it is sensitive enough to detect as many credit frauds as possible and not trigger false alarms for non-fraud transactions at the same time. The module analyzes historical and ongoing credit card transactions, learning to identify those bearing sings of a fraud. The module also generates ranking of suspicious transactions which can then be analyzed by your employees. The solution can be easily integrated with your existing systems through an API.

Credit Card Fraud detection system benefits

Improved credit card fraud detection rate while keeping false alarms at low level
Increased protection of your customers
Real-time detection – immediate action can be taken to prevent fraudulent transactions
Easy integration with your existing solutions through APIs

Proof of concept

Based on hundreds of thousands transactions, our internal research has shown that with Comarch Fraud Protection AI modules, around 90% of credit card frauds can be identified correctly. Furthermore, almost all “clean” transactions were classified properly, which is a phenomenal result compared to typical rule-based approach.

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