Algorithms built to detect credit card fraud can deny charges if they perceive the attempted purchase is unfamiliar or unusual based on historical data, even when a person has enough credit to make the purchase.

Financial institutions check for lots of things before authorizing a purchase

Before authorizing a purchase, card issuers consider whether or not the cardholder has a high enough available credit limit to make the purchase, where the purchase is being made in order to reduce fraud, and more.

Why there are microchips in credit cards

Credit cards have evolved significantly since their invention in 1950, but security lagged in the US for years. Despite handling only a quarter of global transactions, the US once accounted for half of all credit card fraud. The 2013 Target data breach helped push the adoption of chip cards, reducing fraud but shifting risks to online scams.

Microchips in payment cards improve security by generating unique transaction codes

Magnetic strips store unchanging data that is easily cloned, but microchips act as miniature computers that encrypt sensitive data. They also resist fraud by creating data unique to each transaction, preventing duplication.

On credit and debit cards, magnetic stripes have been slowly replaced by microchips that use dynamic, encrypted data to prevent card cloning and counterfeit fraud.

AI could be increasing the opportunities for financial fraud

Systems that can reproduce near perfect counterfeits of legitimate financial documents are increasingly prevalent and sophisticated. The international Association of Certified Fraud Examiners estimate organizations lose about 5% of revenue to fraud each year.

Watch a video that visualizes what really happens inside a credit card network when you make a purchase.

Deepfakes images can be identified by visual elements that do not match real-world phenomena, including asymmetric eyes or earrings, surreal backgrounds, and indecipherable background text.

Smart pricing algorithms can unintentionally work together to raise prices

Researchers have found that, through trial and error, algorithms designed to adjust prices based on market data can learn to mimic price-fixing tactics, even without explicit programming to do so. Over time, they "learn" that it is more profitable to avoid price wars and keep prices high when competitors lower prices.

Privacy concerns in the credit scoring system

Because credit scores use a lot of personal information (such as whether or not someone makes credit card payments on time), some worry their privacy is being compromised in the data collection process.

Credit monitoring services may oversell the benefits of dark web monitoring

Currently, technology exists to scrape and download data from webpages, which can then be analyzed to identify a user's information. However, scrapers cannot access or remove personal data from dark websites because they are often protected by paywalls, rendering monitoring ineffective beyond reporting publicly available data breaches.

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

Gas stations top the list of riskiest places to use a debit card

Unlike credit cards, debit cards withdraw funds directly from your checking account, and federal laws provide fewer consumer protections against debit fraud. Gas station "skimmers" and long periods of separation from the card at restaurants are some of the more vulnerable spots for use.

Fraud and identity theft are among the most common dark web-related crimes.

Audio deepfakes have fooled voice biometrics at banks

Limitations in facial and body movements as well as mismatches between audio and lip movements can sometimes help flag a video as a deepfake, but removal of the video component significantly improves AI mimicry.

Malicious use of deepfakes can be found in scams, misinformation, and disinformation

Artificial neural networks can identify an individual's physical characteristics when provided with training data, such as real footage of the person speaking. Graphics techniques overlay these with matching characteristics from another real or generated person to create novel footage of things that have never taken place in reality.

Bias can manifest in algorithmic decisions because the program follows rules that may have been written by individuals unaware of their own biases.

Charge cards are different from credit cards

Unlike credit cards, charge cards often don’t have a preset credit limit, but cardholders need to pay off the entire balance every month.

Affirm's COO recently said that the company believes the credit card is 'going the way of the VHS tape'

Some people use buy now, pay later apps as an alternative to credit cards. BNPL services have a lower barrier to entry than credit cards—many frequent BNPL users have low credit scores.

Why consumers could soon see the impact of buy now, pay later loans on their FICO credit scores

FICO rolled out its first-ever BNPL-specific credit scoring models in June 2025. While missed installments can now negatively impact a credit score, there's also the potential for on-time BNPL installment payments to help boost scores.

The Consumer Financial Protection Bureau is looking closely at BNPL

Given the lack of data around BNPL services in comparison to other financial products such as credit cards, the CFPB has conducted research to better understand BNPL products and how they could harm and benefit consumers.

Some argue that credit reporting promotes economic inequality in the US

Although today's credit scoring system is much more objective than, say, the credit assessment system of the 1700s, some argue there's still room for improvement, with critics saying the current system is still discriminatory. For example, while mortgage payments have long been automatically reported to credit bureaus, rent payments (the primary housing expense for most lower-income Americans) historically haven't been reported unless a payment was missed.

Merchants have to pay credit card transaction fees each time a card is swiped

Credit card networks such as Visa, Mastercard, and American Express must calculate fees for not only the customer, but also the merchant in every transaction.

An explanation of hard credit checks vs. soft credit checks

Some buy now, pay later services do a soft credit check rather than a hard credit check, like credit card companies would do when someone applies for a card. While hard credit checks can impact your credit score, soft pulls typically do not.

Unlike the models made by large AI companies, open-weight models can have their safeguards removed or weakened, a process that has become increasingly easy and may expose users to harmful information.

Fair use may shield AI companies from potential copyright violations

AI models are trained on enormous datasets built from content scraped from websites across the internet. Copyright law includes a four-factor test to determine whether copies of content fall under fair use, but the law's enforcement has been inconsistent.

Machine learning lets computers learn from data instead of relying on programming

ML is a core part of artificial intelligence and is foundational to applications like chatbots, fraud detection, and self-driving cars. Businesses are also adopting it to improve efficiency and identify patterns in data, though the latter may be inaccurate.

Customers wrote just one check a year in 2024, on average

Major retailers like Target have stopped accepting checks in recent years as real-time payment systems are increasingly being implemented across the US. This shift is considered an improvement in safety—as checks are vulnerable to fraud—but can lead to some exclusion for those without access to digital devices.

Large language models are examples of foundation models

Such models are trained on massive amounts of data, which enable them to perform a variety of tasks, rather than being provided task-specific data to complete a narrow function. Such models come with high compute costs and potential trust issues due to the nature of their unorganized training data.

AI models incorporate a randomness parameter when generating responses to prompts

AI models use this parameter to prevent repeated outputs by sometimes choosing less likely next words during sequential generation. However, this randomness—alongside insufficient data and training—may cause hallucinations of incorrect results.

Managing too many credit cards can be costly if they have annual fees

However, canceling cards must be strategic if it becomes necessary, as it can negatively impact your credit score. Before canceling, experts suggest asking if it's possible to downgrade to a no-annual-fee version in order to keep the account open, thus preventing potential hits to your credit score.

American Express runs on a 'spend-centric' model, using perks and merchant fees to drive profit

American Express cardholders tend to spend more money per transaction than other credit card users, which makes the interchange fees merchants have to pay to accept American Express cards worth it while incentivizing other companies to pay the interchange fee as well.

AI hallucinations are inaccuracies or falsehoods in generated content

AI hallucinations are a by-product of the sequential creation of content based on patterns in text, which may not reflect the context or meaning behind a prompt. Back-end issues like biased datasets and overfitting of model data contribute to hallucinations.

One in five Americans has an error on their credit report, according to the Federal Trade Commission

In 2026, four US senators sent letters to two of the nation's major credit bureaus (TransUnion and Experian) after an investigation revealed that they were fixing fewer errors on consumers' credit reports.

AI tools can reinforce existing societal biases—especially racial ones—because they are trained on human-created data containing implicit and explicit biases.

Trick a large language model into revealing secret passwords over multiple rounds, each with increasing safeguards.

Credit card companies make money through interest, annual fees, and more

They also profit from interchange fees, the fee the retailer has to pay the credit card network in order to facilitate a transaction. The fee is typically between 1% and 3% of the transaction amount.

Hackers once used 'Fortnite' to clean money from stolen credit cards

Hackers using stolen credit cards would create "Fortnite" accounts and purchase V-bucks, the in-game currency. The hackers would then sell the entire "Fortnite" account on sites like eBay. For instance, an account with $100 worth of stolen V-bucks might sell $25 on eBay.

Financial institutions use 'Know Your Client' standards to prevent money laundering

KYC standards include verifying customers' identities and conducting regular account checks for high-risk clients to avoid money laundering schemes. These standards obligate banks to report suspicious customers and activity.

Listen to a podcast episode from 1440's editor in chief that explains how a credit card transaction works behind the scenes.

When you buy even something as small as a $5 latte, a global relay between banks and networks like Visa and Mastercard is triggered. It all started with a 1958 campaign from Bank of America that jump-started the credit card revolution.

Old credit cards can be made into guitar picks, phone stands and more

Experts recommend always cutting through the chip and the card number when disposing of expired credit cards, as the magnetic strip, the chip, and the card number all contain information that thieves might be interested in.

Contactless credit cards descend from a batteryless Cold War listening device

The same physics that let Soviet spies eavesdrop on the US ambassador in 1945 underlie contactless payment methods today. The device is a passive chip powered wirelessly by incoming radio waves.

How AI is revolutionizing banking

Generative artificial intelligence has left no industry untouched, and banking isn’t an exception. Experts estimate that gen AI could add as much as $340B in value to the banking sector each year. For consumers, this involves new features to prevent fraud and customer service chatbots. This report dives into the revolution underway and seeks to define a “full stack” AI bank of the future. Read more here.

In 2025, American Express paid $230M to settle fraud and deceptive marketing charges

American Express launched a wire payment service targeted at small businesses and falsely touted the service as a tax-deductible business expense in 2018. American Express was also accused of allowing small businesses to open corporate credit cards without the necessary employer identification number (EIN).

Credit card issuers determine your credit limit using your payment history, income, and more

Some credit card issuers also take your other payment obligations into account when determining your credit limit, such as the cost of your rent or alimony.

China is making a deliberate push towards becoming a cashless society, making digital payment services like Tencent's WeBank even more valuable.

American Express is targeting Gen Z, who are generally less risk-averse than older generations

Unlike Millennials, who grew up during the Great Recession in 2008, Gen Z has a higher risk appetite and has grown up with more gamified spending platforms, like online gambling and prediction markets. Analysts say Gen Z's risk appetite makes them prime targets for credit card companies like American Express that can sell younger customers on large benefits in exchange for annual fees.

BNPL may be adding a layer of debt that doesn't show up in economic data

Given somewhat limited regulation in the BNPL space, data about its usage is limited—many BNPL firms have resisted sharing information about user purchases, finances, and more thus far. As more people begin using buy now, pay later services, experts are concerned about whether BNPL is adding a new layer of "hidden leverage" to the economy.

Ethereum Founder Vitalik Buterin was surprised by the emergence of NFTs

Scams have pervaded the NFT space and made critics question their legitimacy, but proponents believe their use cases could one day extend to real estate and credit scores.

Over 25% of Americans regret using BNPL due to unexpected costs

Almost 24% of buy now, pay later users have missed payments, with younger users (32% of 18- to 29-year-olds) being more likely to fall behind on payments than their older counterparts. (Only 12% of adults age 60 or over had done the same.)

The history of credit cards begins with the 'Fresno Drop.'

On a 1958 morning in Fresno, California, about 60,000 people woke up to find a strange rectangular piece of plastic on their doorstep without explanation. Those chosen for the "Fresno Drop" didn't know it yet, but Bank of America was behind the credit card delivery stunt.