Credit card fraud is one of the leading crimes of modern times. This fraud is often committed by individuals against financial services providers, with nearly two-thirds (62%) of financial institutions seeing increases in financial crimes in the past year. In fact, the U.S. payment card industry is expected to lose over $165 billion over the next decade.
Nearly two-thirds of U.S. credit card holders experienced card fraud at least once as a cardholder, according to Security.org. About 151 million Americans were affected in 2022, a sharp rise over the 127 million Americans counted in 2021.
Credit card fraud continues to grow and affect customers and businesses alike, and requires stricter security measures. AI systems with machine learning, predictive analytics and deep learning to identify and prevent credit card fraud are the best solution for the rising issue.
What is credit card fraud?
Financial services providers must look out for fraudulent activities in credit card activities. Examples by perpetrators include:
- Counterfeit fraud – creating fake credit cards with stolen information.
- Lost or stolen card fraud – using a card that has been reported as lost or stolen.
- Card not present (CNP) fraud – using stolen card information to make purchases online or over the phone.
- Skimming – stealing card information through a device that reads the magnetic strip.
- Phishing – tricking people into revealing their credit card information through fake websites or emails.
Any of these tactics can create havoc for the financial institution and individual, involving:
- Identify theft, in which a bad actor creates an alternate identity while posing as the cardholder.
- Account takeover, where a malicious party accesses a person’s credit card account. The criminal then hacks the account with information changes.
- Application fraud, where scammers create synthetic identities to apply for new credit cards.
AI-based systems can help banks, credit unions and other financial services providers to detect, reduce and prevent credit card fraud.
How can financial services providers prevent fraud using AI?
Financial institutions should implement robust risk management policies, procedures and technologies to help lower the risk of fraud, such as transaction limits, activity monitoring, biometric authentication, tokenization and more. Most importantly, financial institutions can use advanced analytics and machine learning algorithms to analyze large amounts of transaction data, identifying patterns and trends that show fraudulent activity.
For example, Fraud.net leverages collective information about fraud attempts, persons of interest and new fraud trends, in their collaborative data consortium. With consortium data, AI and machine learning models can use entity analysis and deep learning tools to track enormous amounts of data to uncover any peculiar transactions or patterns at play.
Fight Fraud with Fraud.net
Fraud.net can help. Our AI-powered fraud management solutions are easy to implement, customizable and affordable for financial companies of all sizes. Choose from a wide range of AI-based fraud detection and prevention programs to protect your organization against credit card fraud.
Learn more about Fraud.net’s solutions for banks, credit unions and other financial services providers. Our sophisticated technology for fraud detection, prevention and analysis can help detect fraud and beat other systems that rely on tried and true tactics.
Want to get started with preventing credit card fraud? Schedule a demo with one of our solutions consultants today.