How machine learning can support fraud detection in online payments

The pandemic accelerated digital payments and online commerce, but also contributed to rising payment fraud, with global losses projected to exceed US$343 billion by 2027 through methods such as card fraud, identity theft, malware, and phishing. Traditional protections, including fixed rules, manual reviews, and behavioural analytics, can identify suspicious transactions but may create false positives that delay legitimate purchases and frustrate customers. Machine learning offers a scalable alternative by analysing transaction histories and real-time signals such as purchase amounts, locations, devices, IP addresses, and frequency to detect anomalies, block suspicious activity, or request additional verification. As models learn from fraud reports and new patterns, they can improve accuracy, reduce unnecessary reviews, and adapt to growing transaction volumes. Effective implementation should combine machine learning with rule-based controls, use diverse training data to limit bias, and balance security with customer privacy and convenience through frictionless authentication. Airwallex states that its payment solutions use machine learning-based fraud detection and regular quality checks to identify and prevent suspicious transactions while supporting a smoother customer experience.

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