Types of fraud and how to mitigate risk

Online fraud is becoming more complex as new payment methods and technologies emerge and hackers become more sophisticated. Businesses should leverage advanced fraud prevention technologies if they want to help protect their organizations and customers from cyber attacks.

Emerging types of fraud

As online shopping grows, so does payments fraud, leaving customers and business vulnerable to cybersecurity threats. According to the Federal Trade Commission (FTC), U.S. consumers submitted 2.6 million fraud reports in 2023, amounting to $10 billion lost — up from 2.5 million reports of fraud and $9 billion lost in 2022.¹ Meanwhile Juniper Research predicts that global business losses from online payment fraud will exceed $362 billion between 2023 to 2028.²

So, what different types of fraud should customers and businesses look out for? Common types of fraud include:

• Payment fraud, using stolen card details and/or access to compromised accounts.

• Account takeover (ATO) fraud, to steal saved payment and personal information to sell it on the dark web.

• New account fraud (NAF), where stolen and synthetic identities are used to fraudulently open new lines of credit.

• Friendly fraud, when legitimate customers file chargeback claims saying they have not received orders that, in fact, have been delivered.

Machine learning algorithms and fraud prevention solutions

With fraud on the rise, it's essential for businesses to adapt advanced fraud prevention and risk mitigation strategies. What is fraud prevention, exactly? Fraud prevention is the process of using tools and strategies to help detect and reduce incidents of fraud.

Going beyond just strengthening their passwords and Wi-Fi network security, organizations can leverage advanced ML and rules-based solutions to get ahead of cybercriminal activities and protect their data. For example, businesses might consider the following fraud prevention techniques to help keep their systems and their customers' information safe:

ML algorithms can process large datasets with multiple variables, to uncover complex patterns that human eyes might miss, indicative of sophisticated fraud. They’re also able to continually adapt to keep pace with changing patterns. According to S&P Global Market Intelligence, roughly 40% of financial services companies rely primarily on machine learning for fraud detection and financial forecasting.³

Rules-based systems form a solid foundation for fraud prevention but must be written by analysts and can become overwhelmingly complex and cumbersome to maintain as they expand. That said, they can be written to mitigate fraud risk for organizations while ML engines are being trained. Teams could even use ML algorithms to suggest new rules to write.

A two-sided payment network serving up comprehensive, high-quality, fresh data from consumers and businesses is excellent fuel for intelligent ML engines.

This is the value of PayPal’s platform, which has remained at the forefront of the digital commerce revolution for over 25 years, now serving 429 million customer active accounts⁵ globally.

Given the amount of fraud businesses are seeing in recent years, it’s time for a smarter approach to enterprise fraud management.

How to mitigate risk

Along with machine learning solutions, there are many other fraud prevention and detection tools that businesses can use to help mitigate risk.

Some other effective measures to mitigate risk include:

• Chargeback protection. Help reduce the risk of loss from chargeback by asking your payment processor about chargeback protection.

• Network tokenization. Ensure your payment vault encrypts and tokenizes customer information to help keep data secure.

• PCI standards. Build payment processes that meet Payment Card Industry (PCI) regulations to help protect cardholder data.

• Fraud KPI tracking. Stay on top of and ahead of fraud issues by tracking KPIs like dispute rates, approval and decline rates, and authorization rates.

• 3DS. Leverage the latest 3D Secure (3DS) authentication protocols to help validate card transactions and verify users.

PayPal, for example, helps businesses fight fraud and improve risk decisions with advanced solutions and smart technology. PayPal’s Fraud Protection Advanced uses machine learning and analytics to help protect businesses from fraud and adapt to an ever-evolving payments landscape.

Learn more about how PayPal is helping merchants address changing fraud.