Credit card fraud
In 2018, unauthorised financial fraud losses across payment cards and remote banking totalled £844.8 million in the United Kingdom.
However, this type of fraud can be detected through means of artificial intelligence and machine learning as well as prevented by issuers, institutions, and individual cardholders.
Regulators, card providers and banks take considerable time and effort to collaborate with investigators worldwide with the goal of ensuring fraudsters are not successful.
The technology and security measures behind credit cards are continuously advancing, adding barriers for fraudsters attempting to steal money.
Fields can vary, but the most common include the Name of the cardholder; Card number; Expiration date; and Verification CVV code.
In Europe and Canada, most cards are equipped with an EMV chip which requires a 4 to 6 digit PIN to be entered into the merchant's terminal before payment will be authorized.
[7] Given the immense difficulty of detecting credit card fraud, artificial and computational intelligence was developed in order to make machines attempt tasks in which humans are already doing well.
Due to advances in both artificial and computational intelligence, the most commonly used and suggested ways to detect credit card fraud are rule induction techniques, decision trees, neural networks, Support Vector Machines, logistic regression, and meta heuristics.
For example, some "suggest a framework which can be applied real time where first an outlier analysis is made separately for each customer using self-organizing maps and then a predictive algorithm is utilized to classify the abnormal looking transactions."
[8] Touching a little more on the difficulties of credit card fraud detection, even with more advances in learning and technology every day, companies refuse to share their algorithms and techniques to outsiders.
Overfitting means that the computer system memorized the data and if a new transaction differs in the training set in any way, it will most likely be misclassified, leading to an irritated cardholder or a victim of fraud that was not detected.
When an account is opened using fake or stolen documents, the fraudster could then withdraw cash or obtain credit in the victim's name.
Victims are often the first to detect account takeover when they discover charges on monthly statements they did not authorize or multiple questionable withdrawals.
[13] There has been an increase in the number of account takeovers since the adoption of EMV technology, which makes it more difficult for fraudsters to clone physical credit cards.
[14] Among some of the most common methods by which a fraudster will commit an account, takeover includes proxy-based "checker" one-click apps, brute-force botnet attacks, phishing,[15] and malware.
[17] Social engineering fraud can occur when a criminal poses as someone else which results in a voluntary transfer of money or information to the fraudster.
A common tactic is sending spoof emails impersonating a senior member of staff and trying to deceive employees into transferring money to a fraudulent bank account.
[21] Common scenarios for skimming are taxis, restaurants or bars where the skimmer has possession of the victim's payment card out of their immediate view.
[22] The thief may also use a small keypad to unobtrusively transcribe the three or four-digit card security code, which is not present on the magnetic strip.
The US Department of Justice announced in September 2014 that it will seek to impose a tougher law to combat overseas credit card trafficking.
Authorities say the current statute is too weak because it allows people in other countries to avoid prosecution if they stay outside the United States when buying and selling the data and do not pass their illicit business through the U.S.
The Department of Justice asks US Congress to amend the current law that would make it illegal for an international criminal to possess, buy or sell a stolen credit card issued by a U.S. bank independent of geographic location.
The Code of Conduct for the Credit and Debit Card Industry also applies to payment networks, ensuring transparency and fair treatment in the handling of merchant and consumer accounts.
The Australian Transaction Reports and Analysis Centre has established standard definitions in relation to identity crime for use by law enforcement across Australia: Given increasing number of unauthorised payment card transactions involving frauds and scams, the Hong Kong Monetary Authority issued two Circulars on 25 April 2023.
[citation needed] Often enough online merchants do not take adequate measures to protect their websites from fraud attacks, for example by being blind to sequencing.
In contrast to more automated product transactions, a clerk overseeing "card present" authorization requests must approve the customer's removal of the goods from the premises in real time.
Mail Order/Telephone Order (MOTO) merchants are implementing agent-assisted automation which allows the call center agent to collect the credit card number and other personally identifiable information without ever seeing or hearing it.
[45] From 16 July to 30 October 2013, a hacking attack compromised about a million sets of payment card data stored on computers at Neiman-Marcus.
[48] On 15 May 2016, in a coordinated attack, a group of around 100 individuals used the data of 1600 South African credit cards to steal US$12.7 million from 1400 convenience stores in Tokyo within three hours.
By acting on a Sunday and in another country than the bank which issued the cards, they are believed to have won enough time to leave Japan before the heist was discovered.
