One of the biggest hurdles in the credit card segment is the hackers garnering illicitly obtained Big Data of credit card information globally using sophisticated techniques to satisfy their financial needs that normally go unnoticed when they make purchases using the card.
Improving technologies promise deterrents to credit card fraud, However, the stakes are very high and it is a never-ending battle since hackers are not only knowledgeable in the very same techniques but innovate their methodologies very frequently. Compromised credit card information has led to e-commerce companies, banks and credit card companies needing highly technical software solutions to avoid losing billions of dollars.
Among the very many solutions offered the ones based on technology handling the credit card’s big data analytics courses is the safest bet. The adage prevention is better than cure applies well here and smart techniques in ML with Big data systems can counter such frauds.
The typical process:
Financial institutions that issue credit cards or companies that offer credit-facilities usually create an exhaustive user profile of their customers. Details indicated here are the user mobile number, call center conversations, social media accounts, data from customer’s devices and more helping the analytics systems collect and use information from multiple origins and sources. The AI behind the scene analyses typical customer behavior and involves very large big data analytics courses and Big Data sets across disparate sources.
A red flag is raised if any unusual or deviation from normal buying patterns is observed. The customer will then receive an immediate alert seeking information if such transaction was indeed made by them.
Previously phone calls were made. But currently, automated messages serve the purposes of both privacy and personalization.
Typical red flag scenarios pointing to illegal and shady activities that immediately arouse suspicions are:

Use of a new unrecorded device for credit card transactions.
Several rapid transactions happening using different devices in

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