How machine learning can support fraud detection in online payments

The pandemic catalyzed the digitization of many businesses and changes in customer shopping behavior. Through this, digital payment transaction volume grew by 25 percent globally between 2019 and 2020.¹ While the growth in online transactions has had many benefits for businesses, it has also raised a new challenge: increased online payment fraud.

The ripple effect of a single fraudulent incident can be far-reaching. It affects both customers and your business, which is why fraud detection is important for proactive payment fraud prevention. Artificial intelligence (AI) technology, like machine learning (ML), can identify and prevent real time fraud detection, helping safeguard global payments, protect customers from payment fraud, and ensure long-term business success.

The scale of online payment fraud

As more businesses accept digital payments, online payment fraud is growing. Losses are projected to exceed $343 billion by 2027² due to activities like credit card fraud, identity theft, malware, and phishing.

The impact of online payment fraud on your business can be devastating, not only in terms of financial losses but also for your brand reputation. It can directly impact your bottom line through lost revenue from fraudulent transactions, chargeback fees, and legal expenses.

For customers, the impact is just as severe. They can lose money, have their sensitive information or bank account details stolen, and lose customer trust in your brand. When customers fall victim to fraud, they may avoid transacting with your business again and become wary of online payment systems altogether.

Using strong fraud prevention measures and advanced fraud detection tools is essential to protect your customers' payment information and prevent payment fraud.

Online payment fraud detection methods

Traditional online payment fraud detection relies on rule-based systems, manual reviews, and behavioral analytics to catch fraudulent activities.

The rule-based system involves setting specific rules to flag suspicious transactions within the payment systems. Analysts manually review the transaction to determine if fraud is present. Behavioral analytics, which tracks patterns in user behavior, also detects unusual activity that may indicate attempts to commit fraud.

While traditional methods provide some protection, they can result in false positives, flagging legitimate purchases as fraud. This requires further verification, leading to payment delays, frustrated customers, and lost revenue.

Rise of machine learning in fraud detection

More recently, machine learning has emerged as a scalable, adaptive approach to stop online payment fraud. ML models can analyze massive volumes of payment details in real time without manual intervention, enabling accurate, continuous monitoring of transactions.

No matter how many online payments your business processes, machine learning models can evolve to detect new fraud patterns and support fraud systems in real-time.

How machine learning works in online payment fraud detection

Each time a transaction is initiated, fraud detection tools work silently in the background.

Machine learning rapidly compares the current transaction with historical data, analyzing factors such as transaction amount, payment credentials, geolocation, device type, IP address, and velocity (e.g., whether multiple unauthorized transactions are occurring within a short period). If any anomalies arise, the system flags the transaction as suspicious and may block it or trigger multi-factor authentication.

ML models also adapt to fraud performance feedback. When fraudsters gain access using new techniques, such as account takeover, friendly fraud, or use of stolen credit card information, ML algorithms retrain on this relevant data to keep defenses current.

Benefits of using machine learning in payment fraud detection

Machine learning supports ‌payment fraud detection by identifying patterns and uncovering unusual behavior as it takes place. Over time, these models improve their accuracy and effectiveness by continuously learning and adjusting to new patterns and scenarios, thereby refining their understanding of what constitutes normal behavior versus what is considered suspicious.

Machine learning can:

• Detect fraudulent transactions in real time

• Adapt continuously to new fraud patterns

• Scale with your transaction volume without slowing down

• Reduce false positives, preserving the customer's identity and experience

• Improve accuracy as more fraud risk scores and feedback data are collected

Considerations for machine learning to improve payment fraud detection

While powerful, machine learning isn't perfect. It’s essential to strike a balance between fraud controls and user experience, while ensuring secure authentication.

For example, if training data only includes urban transactions, the model might incorrectly flag rural activity as fraud. To avoid biased fraud detection, it’s critical to use diverse datasets that reflect a broad customer base and different transaction types, like mobile payments, card-not-present transactions, and purchases across regions.

Businesses must also ensure compliance with regulations like anti-money laundering (AML) policies and educate customers on protecting their login credentials and avoiding business email compromise attempts.

The role of financial institutions and payment service providers

As fraud tactics evolve, financial institutions and payment service providers are expected to stay ahead of increasingly complex threats. By embedding fraud detection tools powered by machine learning into their online payment systems, they help prevent a wide range of fraudulent activities, including chargeback fraud, refund fraud, identity theft, and account takeover.

These systems continuously monitor payment details and user behavior to flag suspicious transactions, especially in card not present transactions and mobile payments. By blocking unauthorized transactions and attempts to commit fraud – such as using stolen credit card information or creating counterfeit cards—these providers help protect both businesses and customers from financial losses and lost revenue.

Their role in payment fraud prevention is crucial to building customer trust, safeguarding sensitive information, and helping businesses ensure compliance with anti money laundering regulations.

How Airwallex can help with payment fraud detection

Airwallex’s global Payments solutions use machine learning-powered fraud detection systems to improve the security of your online payments. Our fraud prevention engine efficiently identifies and blocks suspicious activity by analyzing transaction patterns in real time. This proactive approach not only helps safeguard your business from fraud but creates a more reliable payment experience for your customers, driving loyalty and online sales.

Sources:

¹https://www.mckinsey.com/industries/financial-services/our-insights/sustaining-digital-payments-growth-winning-models-in-emerging-markets

²https://www.juniperresearch.com/press/online-payment-fraud-losses-to-exceed-343bn/