Use Big Data To Mitigate Credit Card Fraud

When it comes to implementing and using high-end ultra-modern technology, it seems like fraud and cybercriminal activities are never going to end. Malicious activities are increasing with each passing day with the rise of cutting-edge technology, as it has become easier to get credit card details.

Nowadays the counterfeit transactions are rising, as most credit card companies are striving to find a robust solution to the credit card problem. Several credit card firms have a great interest in recognizing fraudulent financial transactions.

As per sources, the citizens in the United States paid off 26.2 billion in 2012 using credit cards, and the approximate loss accounted for that year was $6.1 billion due to several non-authorized transactions. And by the end of 2020, the United States witnessed approximately $11 billion in losses due to credit card fraud.

Therefore to put an end to such criminal activities and mitigate the loss of billions, several credit card companies and banks combined forces to leverage the big data tech as it is the best way to fight credit card fraud. Before we go into how big data can help evade credit card fraud let’s understand the basics.

Topics to cover

Introduction to Credit Card Fraud
Big Data: A Boon for Credit Card Companies
How can Big Data Tech Identify Credit Card Frauds?
Challenges faced by Credit Card Companies
The Bottom Line
Introduction to Credit Card Fraud

To put it simply, credit card fraud can be defined as using credit cards or debit cards without any authorization with malicious intentions to acquire funds. And several players in this process can be the potential victim to fraudsters, such as:

Card issuers
Cardholders
Payment gateway providers
Banks
Credit card payment systems
Payment processing firms, etc.
So, it is clear how credit card fraud affects consumers, issuers, and merchants as its economic cost goes way ahead of the cost of illicitly bought merchandise. Companies spend millions to secure themselves from scams.