# Credit card fraud Canonical: https://web4.xpay.bi/en/wiki/wp-en-15905419/ Language: en Published: 2026-10-30T19:03:50.052Z Updated: 2026-10-30T19:03:50.052Z Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. The purpose may be to obtain goods or services or to make payment to another account, which is controlled by a criminal. The Payment Card Industry Data Security Standard (PCI DSS) is the data security standard created to help financial institutions process card payments s ## Key facts ## Overview Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. The purpose may be to obtain goods or services or to make payment to another account, which is controlled by a criminal. The Payment Card Industry Data Security Standard (PCI DSS) is the data security standard created to help financial institutions process card payments securely and reduce card fraud. Credit card fraud can be authorised, where the genuine customer themselves processes payment to another account which is controlled by a criminal, or unauthorised, where the account holder does not provide authorisation for the payment to proceed and the transaction is carried out by a third party. In 2018, unauthorised financial fraud losses across payment cards and remote banking totalled £844.8 million in the United Kingdom. Banks and card companies prevented £1.66 billion in unauthorised fraud in 2018. That is the equivalent to £2 in every £3 of attempted fraud being stopped. Credit card fraud can occur when unauthorized users gain access to an individual's credit card information in order to make purchases, other transactions, or open new accounts. A few examples of credit card fraud include account takeover fraud, new account fraud, cloned cards, and cards-not-present schemes. This unauthorized access occurs through phishing, skimming, and information sharing by a user, oftentimes unknowingly. 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. According to a 2021 annual report, about 50% of all Americans have experienced a fraudulent charge on their credit or debit cards, and more than one in three credit or debit card holders have experienced fraud multiple times. This amounts to 127 million people in the US who have been victims of credit card theft at least once. Regulators, card providers and banks take considerable time and effort to collaborate with investigators worldwide with the goal of ensuring fraudsters are not successful. Cardholders' money is usually protected from scammers with regulations that make the card provider and bank accountable. The technology and security measures behind credit cards are continuously advancing, adding barriers for fraudsters attempting to steal money. ## Means of payment card fraud There are two kinds of card fraud: card-present fraud (not so common nowadays) and card-not-present fraud (more common). The compromise can occur in a number of ways and can usually occur without the knowledge of the cardholder. The internet has made database security lapses particularly costly; in some cases, millions of accounts have been compromised. Stolen cards can be reported quickly by cardholders, but a compromised account's details may be held by a fraudster for months before any theft, making it difficult to identify the source of the compromise. The cardholder may not discover fraudulent use until receiving a statement. Cardholders can mitigate this fraud risk by checking their account frequently to ensure there are not any suspicious or unknown transactions. When a credit card is lost or stolen, it may be used for illegal purchases until the holder notifies the issuing bank and the bank puts a block on the account. Most banks have free 24-hour telephone numbers to encourage prompt reporting. Still, it is possible for a thief to make unauthorized purchases on a card before the card is cancelled. ## Prevention of payment card fraud Card information is stored in a number of formats. Card numbers – formally the Primary Account Number (PAN) – are often embossed or imprinted on the card, and a magnetic stripe on the back contains the data in a machine-readable format. 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. However, a PIN is not required for online transactions. In some European countries, buyers using a card without a chip may be asked for photo ID at the point of sale. In some countries, a credit card holder can make a contactless payment for goods or services by tapping their card against an RFID or NFC reader without the need for a PIN or signature if the cost falls under a pre-determined limit. However, a stolen credit or debit card could be used for a number of smaller transactions prior to the fraudulent activity being flagged. Card issuers maintain several countermeasures, including software that can estimate the probability of fraud. For example, a large transaction occurring a great distance from the cardholder's home might seem suspicious. The merchant may be instructed to call the card issuer for verification or to decline the transaction, or even to hold the card and refuse to return it to the customer. ## Artificial and Computational intelligence 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. Computational intelligence is simply a subset of AI enabling intelligence in a changing environment. 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 metaheuristics. Many different approaches may be used to detect credit card fraud. 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." Some problems that arise when detecting credit card fraud through computational intelligence is the idea of misclassifications such as false negatives/positives, as well as detecting fraud on a credit card having a larger available limit is much more prominent than detecting fraud with a smaller available limit. One algorithm that helps detect these sorts of issues is the MBO Algorithm. This is a search technique that brings about improvement through its "neighbor solutions". Another algorithm that assists with these issues is the GASS algorithm. In GASS, it is a hybrid of genetic algorithms and a scatter search. ## Machine learning 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 with outsiders. Additionally, fraud transactions are only about 0.01–0.05% of daily transactions, making it even more difficult to spot. Machine learning is similar to artificial intelligence where it is a subfield of AI and statistics is a subdivision of mathematics. With regard to machine learning, the goal is to find a model that yields the highest level without overfitting at the same time. 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. The most popular programming languages used in machine learning are Python, R, and MATLAB. At the same time, SAS is becoming an increasing competitor as well. Through these programs, the easiest method used in this industry is the Support Vector Machine. R has a package with the SVM function already programmed into it. When Support Vector Machines are employed, it is an efficient way to extract data. SVM is considered active research and successfully solves classification issues as well. Playing a major role in machine learning, it has "excellent generalization performance in a wide range of learning problems, such as handwritten digit recognition, classification of web pages and face detection." SVM is also a successful method because it lowers the possibility of overfitting and dimensionality. Machine learning and deep learning approaches have shown strong potential in fraud detection due to their ability to automatically learn complex behavioural patterns. However, many high-performing models behave as black boxes, generating predictions without meaningful insight into their decision logic. This lack of transparency is problematic in regulated financial environments that increasingly demand explainable artificial intelligence. Regulations such as GDPR further emphasise the right to explanation in automated decision-making, making interpretability a critical requirement. ## Application fraud Application fraud takes place when a person uses stolen or fake documents to open an account in another person's name. Criminals may steal or fake documents such as utility bills and bank statements to build up a personal profile. When an account is opened using fake or stolen documents, the fraudster could then withdraw cash or obtain credit in the victim's name. Application fraud can also occur using a synthetic identity which is similar to the fake documents mentioned above. A synthetic identity is personal information gathered from many different identities to create one fake identity. Once the identity and the account is established, the fraudster has a few different options to take advantage of the bank. They can maximize their credit card spending by spending as much money as possible on their new credit card. Many fraudsters will use the new credit card to purchase items that have a high resale value so they can turn it into cash. ## Account takeover An account takeover refers to the act by which fraudsters will attempt to assume control of a customer's account (i.e. credit cards, email, banks, SIM card and more). Control at the account level offers high returns for fraudsters. According to Forrester, risk-based authentication (RBA) plays a key role in risk mitigation. A fraudster uses parts of the victim's identity such as an email address to gain access to financial accounts. This individual then intercepts communication about the account to keep the victim blind to any threats. Victims are often the first to detect account takeover when they discover charges on monthly statements they did not authorize or multiple questionable withdrawals. 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. Among some of the most common methods by which a fraudster will commit an account takeover are proxy-based "checker" one-click apps, brute-force botnet attacks, phishing, and malware. Other methods include dumpster diving to find personal information in discarded mail, and outright buying lists of 'Fullz', a slang term for full packages of identifying information sold on the black market. Once logged in, fraudsters have access to the account and can make purchases and withdraw money from bank accounts. They have access to any information that is tied to the account and can steal credit card numbers along with Social Security numbers. They can change the passwords to prevent the victim from accessing their account. Cybercriminals have the opportunity to open other accounts, utilize rewards and benefits from the account, and sell this information to other hackers. ## Social engineering fraud 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. Fraudsters are turning to more sophisticated methods of scamming people and businesses out of money. A common tactic is sending spoofed emails impersonating a senior member of staff and trying to deceive employees into transferring money to a fraudulent bank account. Fraudsters may use a variety of techniques in order to solicit personal information by pretending to be a bank or payment processor. Telephone phishing is the most common social engineering technique to gain the trust of the victim. Businesses can protect themselves with a dual authorisation process for the transfer of funds that requires authorisation from at least two persons, and a call-back procedure to a previously established contact number, rather than any contact information included with the payment request. The bank must refund any unauthorised payment; however, they can refuse a refund if they can prove the customer authorised the transaction, or it can prove the customer is at fault because they acted deliberately, or failed to protect details that allowed the transaction. ## Skimming Skimming is the theft of personal information that has been used in an otherwise normal transaction. The thief can procure a victim's card number using basic methods such as photocopying receipts or more advanced methods such as using a small electronic device (skimmer) to swipe and store hundreds of victims' card numbers. 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. 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. Call centers are another area where skimming can easily occur. Skimming can also occur at merchants when a third-party card-reading device is installed outside a card-swiping terminal. This device allows a thief to capture a customer's card information, including their PIN, with each card swipe. Skimming is difficult for the typical cardholder to detect, but given a large enough sample, it is fairly easy for the card issuer to detect. The issuer collects a list of all the cardholders who have complained about fraudulent transactions, and then uses data mining to discover relationships among them and the merchants they use. Sophisticated algorithms can also search for patterns of fraud. Merchants must ensure the physical security of their terminals, and penalties for merchants can be severe if they are compromised, ranging from large fines by the issuer to complete exclusion from the system, which can be a death blow to businesses such as restaurants where credit card transactions are the norm. Instances of skimming have been reported where the perpetrator has put over the card slot of an automated teller machine, a device that reads the magnetic strip as the user unknowingly passes their card through it. These devices are often used in conjunction with a miniature camera to read the user's personal identification number at the same time. This method has been used in many parts of the world including South America and Europe. ## Unexpected repeat billing Online bill paying and internet purchases utilizing a bank account are sources of repeat billing known as "recurring bank charges". These are standing orders or banker's orders from a customer to honour and pay a certain amount every month to the payee. With e-commerce, especially in the United States, a vendor or payee can receive payment by direct debit through the ACH Network. While many payments or purchases are valid, and the customer has intentions to pay the bill monthly, some are known as rogue automatic payments. Another type of credit card fraud targets utility customers. Customers receive unsolicited in-person, telephone, or electronic communication from individuals claiming to be representatives of utility companies. The scammers alert customers that their utilities will be disconnected unless an immediate payment is made, usually involving the use of a reloadable debit card to receive payment. Sometimes the scammers use authentic-looking phone numbers and graphics to deceive victims. ## Phishing Phishing is one of the most common methods used to steal personal data. It is a type of cyber attack in which the attacker acts as a credible person, institution, or entity and attempts to lure the victim into accepting a message or taking action with the specific request. Often, the target of the attack will receive an email or text message about something they would possibly want or need with the hope of tricking them into opening or downloading the message. Phishing increased during the COVID-19 pandemic as the world relied more on electronic communications, with researchers noting "a substantial spike of 667%" in phishing attacks during the first months of the COVID-19 pandemic alone. Moreover, given the significance of health care systems over recent years, health care companies have been main targets of phishing attacks. Such companies possess huge amounts of patient personal data that can be extremely valuable to an attacker. ## Information sharing Information sharing is the transfer or exchange of data between individuals, companies, organizations, and technologies. Advances in technology, the internet, and networks have accelerated the growth of information sharing. Information can be transmitted and treated at speeds faster than ever before. Individuals may lack a sense of how much sensitive personal information they may themselves share on a given day. For example, when purchasing goods online, an individual provides their name, email address, home address, and credit card information, all of which may be stored and shared with third parties for purposes of tracking the individual's future purchases. Organizations may go to great lengths to keep individuals' personal information secure in their databases, but historically hackers have often still managed to compromise organizations' security and gain access to immense amounts of data. One of the largest data breaches occurred in 2013 when hackers targeted the point-of-sale system of American discount retailer Target, compromising the personal data of roughly 40 million shoppers. CNN Business reported that "they either slipped malware into the terminals where customers swipe their credit cards, or they collected customer data while it was on route from Target to its credit card processors." In just one single purchase at the register, masses of personal data is collected which when stolen has major ramifications. The financial market infrastructure and payment system will continue to be a work-in-progress as it continually battles with security hackers. ## Sources - Credit card fraud · Wikipedia: https://en.wikipedia.org/?curid=15905419 Source: Wikipedia contributors — https://en.wikipedia.org/?curid=15905419&oldid=1380721078 Licence: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation notice: Reformatted for XPay; the source may be abridged, restructured or translated. Consult the cited revision for the original.