The digitization of payments has been rapidly accelerated by the COVID-19 pandemic. As a result, electronic payments have become the norm, and it is unlikely that we will return to the old ways of handling transactions. With the majority of payments now being made using credit and debit cards, it is no surprise that credit card fraud has become more widespread than ever before.
According to the US Census Bureau, total e-commerce sales in 2022 were estimated at $1,034 billion, with a 7.7% increase from the previous year. However, along with the increase in online spending comes a rise in fraudulent activity. In fact, credit cards were the most reported payment method vulnerable to online scams in 2022.
In their 2021 Annual Report, Credit Card Fraud revealed that 127 million Americans fell victim to credit card fraud. The median charge was $62, totaling approximately $8 billion overall in attempted charges. People who stored their credit card information in web browsers and used the same card for autopay as they did for everyday spending were more likely to be targeted.
To combat this increasing threat, banks, neobanks, and other financial institutions are turning to innovative technologies such as machine learning (ML) and artificial intelligence (AI) to detect and prevent credit card fraud. These technologies enable institutions to identify fraudulent transactions quickly and alert cardholders before significant financial damage occurs.
Fraud Detection and Fraud Prevention
With the widespread use of cards for various transactions, the potential for data breaches and fraud has increased. Financial institutions are facing the challenge of detecting and preventing fraud effectively. The use of machine learning algorithms, data analytics, and predictive modeling has proven to be successful in identifying suspicious activities and preventing fraudulent transactions.
Credit card fraud can be categorized into two types: card-present fraud and card-not-present fraud.
Card Present Fraud: This type of fraud occurs when a criminal uses a physical card, either stolen or duplicated, to make fraudulent purchases. Criminals may steal cards through robbery, pickpocketing, or mail theft. They may also install card skimmers at payment points to collect card details and produce duplicate cards.
Card Not Present Fraud: In this case, fraudsters use the card details (card number, account holder name, CVV code) without having the physical card in their possession. They may also use account takeover techniques, impersonating the legitimate cardholder to change account information.
Financial institutions employ various security measures to detect credit card fraud. These measures include tokenization, which replaces credit card numbers with randomly generated numbers, and real-time data enrichment tools, which provide additional information on cardholders. Machine learning algorithms analyze patterns and behaviors to detect fraudulent transactions.
How to Prevent Credit Card Fraud?
Preventing credit card fraud is a top priority for financial institutions. One effective method is tokenization, which replaces sensitive credit card information with randomly generated numbers. This process minimizes the risk of exposing sensitive data while still allowing payment processors to process payments securely.
Financial institutions also utilize rules to detect patterns in transactional data. Any suspicious transactions can be flagged and resolved promptly. The availability of extensive transactional data enhances fraud detection capabilities.
The Biggest Fraud Challenges for Institutions
Financial institutions face significant challenges in detecting and preventing credit card fraud. With most transactions now happening online, fraudsters have shifted their focus to online transactions. Financial institutions and networks categorize fraudulent behavior into four types: e-commerce and remote payments fraud, counterfeit fraud, lost and stolen cards, and account takeovers.
Tools for Fraud Prevention and Detection
Financial institutions employ various tools and technologies to combat credit card fraud:
Consumer Transaction Alerts: These alerts notify cardholders of any initiated transactions, allowing them to identify and address fraudulent activities promptly.
Dynamic Passcodes: Cardholders receive one-time unique codes via banking apps, emails, or SMS to verify transactions, ensuring a high level of security and compliance.
Real-time Data Enrichment Tools: These tools enhance customer data with additional information obtained from various sources, aiding in fraud detection.
Machine Learning (ML): ML algorithms analyze cardholder data, identify patterns, and flag suspicious transactions, effectively detecting and preventing fraud.
Know Your Customer (KYC): KYC processes perform identity verification, ensuring compliance and preventing fraud for customers.
New Technologies for Fraud Detection
In addition to existing fraud prevention technologies, financial institutions are adopting new technologies to combat credit card fraud:
Voice Biometrics: This technology uses voice characteristics to authenticate callers, comparing their voiceprints to previously enrolled samples. It automates the authentication process and can identify fraudsters.
Enhanced Knowledge-Based Authentication (KBA): KBA validates cardholder identities against external sources, leveraging personal information to verify individuals. It combines data from multiple sources to create advanced challenge questions.
Adaptive Authentication: This AI-driven risk scoring capability analyzes multiple data channels to provide real-time guidance to agents during calls, helping prevent fraud before it occurs.
Tech to Prevent Counterfeit Fraud When Using a Credit Card
Financial institutions employ additional measures to prevent counterfeit fraud:
Address Verification Service (AVS): AVS compares the billing address used in a transaction with the issuing bank’s address information to verify the cardholder’s identity.
Geolocation: This technology matches a cardholder’s mobile phone location with the transaction location, providing an extra layer of security.
Account Takeover Tools: Tools such as biometric authentication and activity analytics help detect and prevent account takeovers by verifying identities and analyzing online behaviors.
By leveraging these technological advancements, banks and financial institutions can enhance their fraud prevention capabilities and provide cardholders with secure and seamless digital payment experiences.
In conclusion, credit card fraud is an ongoing challenge in the digital age. However, with the implementation of advanced technologies and effective fraud prevention strategies, financial institutions can detect and prevent fraudulent activities, ensuring the security and trust of their customers.