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Introduction
Credit card fraud is a growing problem affecting millions of people worldwide. As technology advances, criminals are finding new ways to steal credit card information and make fraudulent purchases. This illegal activity results in billions of dollars in losses annually for card issuers, merchants, and consumers.
To combat this issue, Mastercard has developed a new artificial intelligence system that better detects compromised credit cards before fraud can occur. This AI analyzes transactions in near real-time to identify patterns consistent with a breach. The system can spot compromised cards before the first fraudulent purchase, allowing issuers to proactively notify customers and reduce fraud rates.
This innovative technology marks a major advancement in fraud prevention. By leveraging AI, Mastercard aims to stay ahead of criminals and create a safer, more secure payment ecosystem. This article will provide an in-depth look at how Mastercard's AI system works, its key benefits, and the future of AI in fighting credit card fraud.
How Credit Card Fraud Occurs
Credit card fraud is on the rise as criminals become more technologically sophisticated. There are several common ways fraudsters obtain credit card information and make fraudulent purchases:
Skimming devices - Criminals attach skimming devices to ATMs or gas pumps to secretly record card numbers and PINs. The skimmers copy data from the card's magnetic stripe as it is swiped. These devices are difficult to detect visually.
Phishing scams - Fraudsters send fake emails or operate convincing websites to trick consumers into revealing account logins, card numbers, expiration dates and security codes. The scammer poses as a legitimate company, often a financial institution or retailer.
Data breaches - When merchants or financial institutions experience security breaches, millions of credit card numbers can be stolen at once. Recent high-profile breaches have occurred at major retailers like Target, Home Depot and Michaels. Stolen data ends up sold on black market websites.
Criminals can quickly rack up fraudulent charges once they obtain card data through these methods. It's a constant battle for financial institutions to identify fraudulent transactions before the damage is done.
Previous Fraud Detection Methods
Before Mastercard implemented their AI system, credit card companies relied on manual reviews and rule-based systems to detect fraud.
Manual Reviews
With manual reviews, fraud analysts would look for suspicious transactions and signs of compromised cards. This involved closely monitoring transaction data and account activity to try to identify patterns that may indicate fraud.
The downsides of manual reviews are that they are time and labor intensive. Fraud analysts can only review so many accounts at once. It is also prone to human error and transactions can be missed if analysts don't notice certain red flags.
Rule-Based Systems
Many credit card companies used rules-based systems to try to automate some of the fraud detection process. These systems have pre-programmed rules that look for certain transaction characteristics.
For example, a rule may flag transactions that are over a certain dollar amount or from a high risk location. Or rules may look for sudden spikes in transaction volume on an account.
The issue with rules-based systems is that they rely on predefined rules. Fraudsters can find ways around the rules over time. The systems also generate a lot of false positives, flagging legitimate transactions as potential fraud.
Mastercard's New AI System
Mastercard has developed a new artificial intelligence system to detect compromised credit cards faster and more accurately. This AI system utilizes machine learning algorithms that analyze millions of transactions in near real-time to identify patterns and anomalies associated with fraud.
The system works by reviewing each transaction and assigning a fraud risk score based on various factors, including the merchant, customer behavior patterns, transaction details, and more. As new transactions occur, the AI model continues to learn and update its understanding of normal vs suspicious activity.
One of the key machine learning algorithms behind Mastercard's AI is neural networks. Neural networks attempt to simulate the workings of the human brain, with interconnected nodes that process and analyze data. The neural network is trained on vast amounts of transaction data, learning to recognize complex patterns that may indicate fraud.
Additional machine learning techniques used include decision trees, clustering, and support vector machines. These algorithms enable the AI to categorize transactions, detect outliers, identify important relationships between variables, and create predictive models.
By leveraging the power of AI and machine learning, Mastercard can now identify compromised cards much faster. Whereas it previously took banks up to 6 months to detect some types of fraud, the AI system can spot suspicious activity in near real-time. This allows banks to quickly take action, preventing criminals from incurring substantial fraudulent charges and minimizing losses.
Benefits of the AI System
Mastercard's new AI system for detecting compromised credit cards offers significant benefits over previous fraud detection methods. The most notable improvements are faster detection times and more accurate identification of fraudulent transactions.
In the past, credit card companies relied heavily on preset rules to flag unusual transactions. However, these rules often resulted in false positives and missed many instances of actual fraud. AI systems can analyze transactions in real-time and identify patterns that may not be obvious to humans. This allows Mastercard to spot compromised cards much quicker, often within seconds of the first fraudulent use.
Faster detection translates into major savings for banks and consumers. When fraudulent charges are identified rapidly, banks can shut down the card and prevent subsequent unauthorized transactions. This minimizes the money lost in the fraud event. For cardholders, early detection reduces the hassle of dealing with multiple bogus charges and getting refunds after the fact.
In addition to speed, AI also enables more accurate fraud detection. Machine learning algorithms become better at identifying real cases of fraud over time as they process more data. By looking at a wider range of factors, AI systems can catch types of fraud that rules-based systems would miss. This results in fewer false positives and a lower decline rate for legitimate transactions.
Overall, AI allows Mastercard to spot and stop fraud much earlier than was possible in the past. This increased speed and accuracy is a major advancement for the security of credit card payments. Consumers can have greater confidence that fraudulent activity on their accounts will be caught quickly before significant damages occur.
Case Studies and Statistics
Mastercard's AI system has already proven effective at identifying and stopping fraud across many of its customer financial institutions. For example, at one bank in Europe, the AI helped catch several compromised cards that were tied to a fraud ring operating out of South America. By analyzing transaction patterns, the AI spotted anomalies that allowed the bank to immediately deactivate the cards, preventing over $250,000 worth of fraudulent charges.
Additionally, one credit union in the United States reported a 42% drop in annual fraud rates after implementing Mastercard's AI fraud detection. This resulted in savings of approximately $520,000 in fraudulent charges that were avoided. The credit union also noticed a significant improvement in false positives, reducing the number of legitimate transactions incorrectly flagged as potential fraud. This helped improve the cardholder experience.
