Be alert! Cross-border e-commerce faces payment risks.

Riskified is a provider of risk control solutions to prevent e-commerce fraud and payment. The report "The Challenge of Credit Card Refusal and Its Response: 2024 Global Observation" released by Riskified shows that consumers are increasingly using refusal as a means to withdraw funds, and about 75% of consumers in Britain and the United States have refused to pay in 2023. According to the report, in addition to the normal demands of consumers, more and more consumers refuse to pay for compensation under false excuses. According to statistics, this global payment fraud will cause businesses to lose 3% of their annual income on average. With the increase of the amount of refusal to pay, the erosion of profits is enough to reach a harmful level. Therefore, cross-border e-commerce has entered an era of chaotic payment models.

At a time when Chinese enterprises are accelerating to go to sea through cross-border e-commerce and other modes, the cross-border payment environment faced by enterprises is very complicated.Generally speaking, a merchant usually needs to accept four or five different payment methods, including credit card payment, electronic wallet payment and real-time account transfer payment. For merchants, it is necessary to balance a variety of different payment methods and guard against the fraud risk of each payment method. In particular, the rapid development of artificial intelligence and other technologies has lowered the threshold for people to commit fraud.

"The development of artificial intelligence technology has enabled fraudsters to find useful tools, but artificial intelligence can also be used by businesses to conduct anti-fraud activities." Tessie Patjas said that artificial intelligence can help businesses to analyze and identify a large amount of data, and monitor risks in real time, and the continuous learning ability of artificial intelligence can adapt to new fraudulent means, and constantly improve the level of risk prevention. For example, artificial intelligence technology can analyze the transaction behavior of multiple accounts, and judge whether they are real users or have malicious fraud from their historical claims, refusal to pay orders, policy abuse and other events.

Tessie Patjas said that recently, some cross-border e-commerce platforms in China launched the "refund only" option, which helped to expand the market quickly or maintain certain competitiveness, but it also increased the cost of merchants and brought higher payment risks. She said that in addition to refusal to pay fraud, Riskified's analysis of merchant data showed that 27% of refund applications were abuse of the return policy. There is also a 2% sharp drop in global income worldwide, which is caused by the abuse of the refund policy.As far as specific industries are concerned, Tessie Patjas thinks that we can focus on the payment risk of digital electronics industry. On the one hand, this industry is developing rapidly, with fierce competition and low profit rate; On the other hand, the industry is particularly favored by fraudsters, because these products are characterized by high selling price, resale, small size and high demand. The risks often encountered in this industry include illegal resale, sending empty boxes back to claim, etc.

In the face of payment risks such as refusal to pay and policy abuse, many businesses can only sit back and watch the loss of income. To this end, Tessie Patjas put forward the following suggestions: First, overseas merchants need to use digital means to formulate risk management strategies, determine the types of transactions that merchants can accept, determine the range of acceptable refusal rates, effectively identify fraudulent or unsafe transactions, and stop them in time; Second, understand the mainstream payment methods in the target market and local policies and regulations, and formulate anti-fraud policies and strategies on this basis; The third is to cooperate with external technical teams and suppliers to improve the efficiency of risk identification and response.