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Request demoCredit card fraud is one of the most common types of identity fraud. Its prevalence rose significantly during the coronavirus pandemic, with fraudulent credit card applications up 17 percent in the first month of the pandemic alone. And this has been sustained since, with the National Fraud Hunter Prevention Service revealing that UK credit card fraud reached a five-year high in the last three months of 2021. With this in mind, how can financial organizations protect themselves and their customers from credit card fraud and minimize its impact on financial institutions worldwide?
Credit card fraud is the unauthorized use of a debit or credit card to make purchases or withdraw cash. In 2021, there were 389,845 reports of credit card fraud in the US with the Federal Trade Commission reporting it to be the most common type of identity fraud affecting people aged 20-39.
Credit card fraud happens when a criminal steals someone else’s credit card information and uses it for their own financial gain.
Traditionally, credit card fraud occurred when a physical card was stolen from the owner. With contemporary credit card fraud, it is increasingly likely a fraudster will obtain a victim’s credit card details, but not the physical card.
The two main types of credit card fraud are:
With application fraud, a fraudster uses illegally obtained credit card information to open a new account in the victim’s name. The criminal may have stolen or bought the victim’s details from the dark web.
With account takeover fraud, a criminal uses a victim’s personal identifying information to take control of their account and misappropriate funds.
Find out more about account takeover fraud in our spotlight blog.
Credit card fraud can be divided into two main categories:
This is becoming more common as digital payments are now the norm. Once the fraudster obtains stolen credit card details, they can carry out multiple incidents of fraud, typically via online transactions. An example of digital CNP fraud is when a criminal makes very large online purchases or bulk purchases of the same item, acting quickly to maximize the time they have before the fraud is discovered. CNP fraud can also occur offline; for example, the fraudster could complete a payment form using the stolen credit card details and email it to the retailer. It can also happen over the phone.
Examples of incidents that can lead to CNP credit card fraud:
This is becoming less common thanks to the advent of chip and PIN.
Examples of incidents that can lead to card-present fraud:
The prevention of credit card fraud should be a top priority for financial organizations as it can have a huge impact on time, resources, and the organization’s reputation. The impact can be even more extreme if disputes are unresolved and the customer reports the institution to the Ombudsman, or equivalent.
In the US, under the Fair Credit Billing Act, credit card firms must refund customers for unauthorized purchases over $50 made before the fraud was detected and reported. This is known as a chargeback. Some card companies have policies that reduce this to $0. The customer must alert the card company within 60 days of the fraudulent transaction taking place, and they must respond within ten days.
The guidelines in the UK are similar. Under the Consumer Credit Act, customers are not liable for the fraudulent use of their credit card, although some banks choose not to refund the first £50 if they feel the customer did not keep their credit card details safe. However, if you are a financial institution, the onus is on you to prove that the customer did not keep their details safe.
Fraudsters will not stop evolving their credit card fraud methods, but firms can empower their customers to proactively avoid becoming victims. It’s also important that customers know how to report credit card fraud as soon as possible.
Firms should encourage their credit card customers to:
Firms can also advise their retailers to be proactive about credit card fraud detection by looking out for red flags such as:
The integration of artificial intelligence (AI) and machine learning (ML) into fraud detection software can lead to substantial enhancements in the prevention and identification of credit card fraud. ML tools are fast and accurate and can process vast amounts of data. With sophisticated transaction monitoring software, ML can predict the likelihood of a transaction being fraudulent and predict future behavior. ML can also help firms keep up with trends and stay one step ahead of new evolutions in credit card fraud schemes. This is vital as it helps firms maintain customer trust and brand reputation and reduce costly chargebacks.
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Request demoOriginally published 24 May 2023, updated 20 September 2024
Disclaimer: This is for general information only. The information presented does not constitute legal advice. ComplyAdvantage accepts no responsibility for any information contained herein and disclaims and excludes any liability in respect of the contents or for action taken based on this information.
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