AI-Powered Fraud in E-Commerce Increased by 33 Percent

Fraud pressure in the e-commerce sector has increased as artificial intelligence tools have become increasingly accessible.

According to data from Signifyd’s 2026 State of Fraud Report, online risk pressure across the company’s Commerce Network increased by 33 percent in the first four months of 2026 compared with the same period last year. The data was obtained from a network consisting of thousands of e-commerce businesses and 950 million unique digital wallets.

Fraud No Longer Targets Only the Checkout Stage

Signifyd CEO and Co-Founder Raj Ramanand said that risky transactions can no longer be addressed only at the checkout stage. Ramanand stated that the rise of artificial intelligence is prompting retailers and financial institutions to reassess trust and identity verification methods at every customer touchpoint.

Artificial Intelligence Reduces the Cost of Attacks

According to the report, widely accessible artificial intelligence tools are reducing the cost and technical complexity of fraud attacks. While this allows malicious actors to operate faster and at a larger scale, it is also increasing both professional criminal activity and first-party abuse carried out by consumers.

Signifyd cited fraud, identity theft, fake websites created to collect personal information, and sophisticated criminal and money-laundering methods involving iPhone devices among real-world cases. The company reported that attackers are using multiple methods simultaneously to increase their gains.

The Line Between Organized Crime and Consumer Abuse Is Blurring

Nicole Jass, Senior Vice President of Business Strategy at Signifyd, stated that the line between organized criminal activity and consumer-driven abuse is becoming increasingly blurred. This shift is pushing e-commerce and retail companies toward more comprehensive risk management systems rather than simply blocking suspicious orders.

Under the new approach, monitoring account activity for signs of potential account takeover, identifying fraudulent return claims without negatively affecting the experience of legitimate customers, and protecting revenue are becoming key priorities. Signifyd uses machine learning to evaluate signals related to identity and transaction intent throughout the entire online shopping journey and generate risk decisions.