Fraud Prevention Strategies Using Payment Data: Insights from Experts
In today's rapidly evolving digital payment landscape, businesses are flooded with vast amounts of transaction data. This wealth of information presents both a challenge and an opportunity—particularly when it comes to preventing fraud. The key to leveraging payment data effectively for fraud prevention lies in understanding how to integrate, analyze, and act on this information across various stages of the transaction lifecycle.
In our latest webinar with the Merchant Risk Council, “Leveraging Transaction Data to Reduce Fraud and Shape Business Strategy,” IXOPAY experts recently shared their insights on how businesses can turn payment data into a powerful tool to detect and prevent fraudulent activities. Here are the key takeaways from their discussion.
Understanding Payment Data
Many businesses today are swimming in data, yet often wading through fragmented insights from separate systems. From customer information and purchase details to transaction routing and settlement timelines, the life cycle of a payment is filled with valuable data points. However, this information is only impactful when connected, contextualized, and applied strategically.
Fraud prevention starts with understanding the complete transaction journey and using that data in a layered, integrated approach. According to our experts, payment data can be broken down into several buckets:
Customer Information: This includes personal details such as name, address, and contact information, which help businesses identify legitimate users.
Purchase Information: This refers to data about the products being purchased, including the SKU, transaction amounts, discounts, and promotional codes used.
Routing Information: This pertains to the details regarding how the payment is being processed—such as which processor is handling the transaction and which merchant account is being used.
Authorization and Settlement Data: Authorization data is used to confirm that the payment is valid, while settlement data tracks when funds are actually transferred.
Post-Processing Data: After a transaction is completed, post-processing information like chargebacks and refunds provides valuable insights into potential fraud patterns.
By understanding these categories, businesses can connect the dots and spot anomalies, trends, or patterns that may indicate fraudulent activity.
Turning Payment Data Into Actionable Insights
The next step in using payment data for fraud prevention is to turn raw data into actionable insights. As experts noted, businesses must focus on understanding the context of each transaction. This involves analyzing not just the customer’s personal information but also contextual data such as:
The device used for the transaction
The location of the transaction
The time of the day the transaction occurred
These contextual clues are vital for recognizing suspicious behavior. For instance, if a customer makes a high-value purchase from a location or device that differs significantly from their usual behavior, it could be an indication of fraud.
The key takeaway is that businesses must continuously analyze and update their fraud detection systems based on the latest transaction data, ensuring their fraud models are dynamic and adaptive.
Layered Fraud Prevention Approach
One of the most effective fraud prevention strategies discussed was the use of a layered approach to payment processing. A layered fraud strategy allows businesses to combine multiple fraud detection methods at different stages of the transaction process.
For example, when a payment is made, the transaction data can be evaluated using a combination of rules-based checks and external risk providers, such as machine learning models. If the transaction still appears suspicious, additional measures such as 3D Secure authentication can be employed to shift liability away from the merchant if fraud is detected.
This layered strategy ensures that no single fraud detection method is relied upon entirely, reducing the risk of false positives or undetected fraudulent activity. The use of dynamic rules and continuous data enrichment through third-party integrations (such as device intelligence, geolocation tools, and address verification) makes fraud prevention more precise and responsive.
The Role of Payment Account References
Another key element in fraud prevention discussed was the Payment Account Reference (PAR). PAR is a technology that links different payment methods (like credit cards, Apple Pay, and Google Pay) to the same underlying account.
This allows businesses to identify the same customer across various payment methods, even if different devices or cards are used. By linking payment methods to a single customer identity, merchants can more effectively detect and prevent fraud.
PAR has exciting use cases to streamline the process of creating meaningful payment analytics. For example, PAR can help inform your risk models. Say you have a subscription-based payment model and a customer with a card on file. However, a new card gets added to the profile of the customer. Without PAR, a new card used for an existing customer would create suspicion and possibly trigger an alert that prevents the transaction from completing.
With PAR, if this new card were issued from the same account, it would be easy to link those two cards together, understanding they reference the same customer. This means you would have higher confidence that there are no concerns with processing this transaction on this known customer profile with a new, unknown payment method.
In addition, the PAR can also play a significant role in streamlining loyalty programs, customer segmentation, and fraud analysis by providing a unified view of a customer’s purchasing history, regardless of the payment method they choose.
Best Practices for Integrating Fraud Detection Tools
Integrating transaction data insights into fraud detection systems can improve accuracy and response times. Here are a few best practices shared by industry experts:
Know Your Data: It’s essential to fully understand the data you have and identify the key insights needed to combat fraud. For example, knowing when fraud chargebacks occur and understanding the type of fraud can help businesses identify which pieces of data are critical to preventing similar incidents in the future.
Test and Iterate: The fraud landscape is constantly evolving. To keep up with new fraud tactics, businesses should continuously test their fraud prevention strategies. This can involve experimenting with different fraud detection rules, integrating new third-party services, and monitoring the effectiveness of these strategies over time.
Use Automation and Machine Learning: By automating aspects of fraud detection, businesses can reduce manual intervention and speed up the response times to suspicious activities. Machine learning and AI models can help detect emerging fraud patterns that traditional rules might miss.
Collaborate with Third-Party Providers: Sometimes, businesses need to enrich their payment data with external data sources such as device intelligence, IP geolocation, or historical fraud data. Partnering with third-party fraud detection vendors can enhance the fraud detection process, making it more comprehensive.
Fraud prevention in payments is no longer just about blocking a transaction based on a simple set of rules. As the digital payment landscape becomes more complex, businesses must leverage all available payment data and fraud prevention technologies to make smarter, data-driven decisions.
By understanding the full spectrum of payment data, adopting a layered fraud prevention strategy, and continuously iterating on their fraud detection models, businesses can not only protect themselves from fraud but also improve their customer experience and optimize payment processes.
In the end, fraud prevention is a dynamic process that requires constant vigilance, collaboration, and a deep understanding of the data at your fingertips.