Fighting Back the Rising Tide of First-Party Misuse

In our last post in this series, we identified the notable increase in first-party misuse impacting merchants of all sizes in nearly all industries. By defining and providing examples of the types of fraudulent behavior categorized as first-party misuse—return abuse, refund fraud, and chargeback fraud—we hope to give your business a leg up in fighting back. 

If history has taught us anything, it’s that fraud only gets more sophisticated over time, and those who commit it are always innovating new ways to get something for nothing. As such, it’s our responsibility to openly communicate with our networks about the fraud trends we see, and to innovate on new ways to protect ourselves. In today’s post, we'll explore the ways you can use your payments data to monitor and combat first-party misuse.

Monitoring Base Metrics

Before you can do something about refund abuse, return fraud, or chargeback fraud, you must first explore whether or not such fraudulent activity is really impacting your business. Start by analyzing the following base metrics for a selected time period:

• Share of Declines - Key predictors for both chargebacks and refunds can be the change in the key decline codes of suspected_fraud, transaction_not_allowed, do_not_honor, and blocked (by you or by your PSP)

• Refund volume - Both the total count and value of refunded transactions

• Refund rate - The number of refunds you’ve processed divided by the total number of processed transactions

• Chargeback volume - Both the total count and value of disputed transactions

• Chargeback rate - The number of chargebacks processed divided by the total number of transactions

Because first-party misuse has significantly increased in popularity among fraudsters in the last year, try to look back at least a year into your data. That way, you can establish expected ranges for each of these metrics while simultaneously monitoring against the baseline for any increases over time. If your refund rate is trending up in recent months, for example, it might be time to dig into your return data. It's also worth looking at both month-over-month and week-over-week changes across different segments in order to tease out the actions that make the most sense; for refunds, you might even look at day-over-day changes! Actions include things like adjusting your fraud rules, changing customer support operations, or even switching up processors.

When we say “dig in,” we mean segmenting out your return or chargeback volume by variables such as payment method, processor, card type, or issuer country (and more). Fortunately, Pagos has everything you need to do just that! 

Chargebacks full dashboard

Chargebacks made simpler with just two charts

Refund rates year over year, last 7 days

Dig into the details

Within our payments data aggregation and visualization platform—Peacock by Pagos—we have interactive dashboards specifically dedicated to Refunds and Chargebacks. In these dashboards, you can quickly narrow down the exact parameters associated with most refunds or chargebacks. For example, you may discover a large proportion of your chargeback volume comes from a single payment method or card type; in that case, you may stop accepting that payment method altogether to avoid this issue, configure the appropriate fraud rules to prevent known bad actors, or even enable 3D Secure as a way of adding friction where there is more risk.

Enriching Your Data

If segmenting your refund and chargeback data helps you identify the primary targets of fraudsters, then finding new ways to segment your data is always worth your time. One way to do this is with metadata. Metadata is a transaction-level label communicated via a merchant's processor at the time of the transaction. Businesses typically assign metadata for custom transaction categorization, for example by a specific product line, customer acquisition channel, or customer cohort. By assigning metadata to your transactions, you can identify the segments of your business most susceptible to first-party misuse. 

A good example of this is using metadata to tag purchases of specific products; with these tags, you can analyze fraud trends at the product level. You may find that a specific popular product (e.g. a trendy insulated cup or style of sunglasses) has above average refund and chargeback rates when compared to the rest of your transaction volume. To combat this, you may impose stricter rules around refunds for these particular items or increase your communication with customers who purchase them, thereby arming your business against potential chargebacks.

Incorporating Data Into Other Workflows

One of the greatest benefits of Pagos is our ability to ingest all your payments data across each processor or gateway you use (often through no-code data connections) and harmonize it into a single data feed. With Peacock, you can view all that data in one place and dig in to find concerning spikes or trends in refund and chargeback volume. But that’s not the only powerful bird we have to join in your battle against first-party misuse—we also have Puffin.

Puffin, our payments data feed, allows you to download your Pagos-harmonized payments data and integrate it into your existing systems and workflows. For example, you can use Puffin to integrate your clean payments data into your call center traffic, essentially powering any call center data analysis with purchase information. You may then identify specific customers who request returns and chargebacks at an unusual rate, and bar them from making any future purchases. This also gives you a way to identify the timing difference between when a transaction occurs and when the associated refund request comes into your call center; the longer this time period, the more likely the return is fraudulent.

You can even enrich your refund and chargeback data in Puffin with data collected outside of the checkout process. If you ask your customers to provide a reason for their returns during the refund process, for example, you can tag associated refunds with these anecdotal return reasons. With all your data in one place, it’s easier for you to identify trends in shady behavior, such as the same customer requesting multiple returns for the same reason, or a large number of customers using the same excuses to abuse your refund or return policies.

Using Puffin to Identify Promotion Abuse

Another type of first-party misuse we didn’t mention in our last blog post is promotion abuse. Promotion abuse happens when individuals take advantage of new customer promotions by creating new accounts with different email addresses to repeatedly receive discounts on purchases. For example, if you offer a promotion of 20% off your first order, a single customer may make multiple purchases from your business, but using a different email address and login details each time to get that discount more than once. This can hurt revenue, when the intention behind taking the “hit” of a 20% discounted order is worth it for the customer’s lifetime value (LTV) or full price purchases. 

Using downloaded payments data from Puffin, you can filter discounted purchases to identify different accounts with the same payment method information (e.g. PAN, expiration date). If you’ve combined your Puffin data with customer account identifiers like IP and shipping address, you can narrow down even further to tag purchases made from different accounts but likely the same individual. You can then build out abuse-preventing rules to stop the same customer from running this play again. Ultimately, the more information you have at your fingertips, the better—especially when it comes to curbing first-party misuse.

Conclusion

Fighting first-party fraud comes down to establishing a feedback loop so you can make the necessary adjustments to your return processes, fraud rules, and chargeback operations. That feedback loop is created by: 

• Leveraging your data

• Establishing baselines

• Systematically breaking it down by key data segments

• Comparing across different time frames

• Integrating those data points into your workflows and configurations

Fraud is intimidating and fighting it can be hard, but making your data an asset in your fight can reduce your costs and losses. Pagos is here to help. Contact us to discuss how our solutions fit your business’ needs and follow Pagos on LinkedIn for more relevant insights!