Strengthening Payment Security - Advance Fraud Mitigation

At Adage, we believe a secure digital experience is essential for building trust and protecting revenue. As fraud techniques grow more sophisticated, we’ve worked with clients to implement multiple layers of protection—3D Secure, the Address Verification System (AVS), Verisoul, and TruValidate—across registration, login, and payment flows. These tools, when combined thoughtfully, help block fraud without getting in the way of real users. 

Below, we break down how each solution was used in real-world projects and share key insights from our development teams.

3D Secure: Adding Identity Verification at Checkout 

Implementing 3D Secure was all about adding an extra layer of protection to reduce fraudulent transactions, particularly for international and high‑volume buyers. By inserting a bank verification challenge into the payment flow, we make it harder for unauthorized users to complete a purchase—and shift liability for any residual fraud to the issuing bank. 

Start with a measured rollout, triggering the challenge only when required by the cardholder’s bank. Once performance is validated, expand to full enforcement for all suspected transactions. Through CMS controls, organizations can easily toggle 3D Secure strictness to adjust the tolerance for risk over time. 

In practice, 3D Secure has led to a significant reduction in fraudulent attempts and chargebacks. During an especially busy ticket‑sale event,  real‑time collaboration with Tessitura’s support team is important to restore full fraud prevention functionality quickly if issues arise. That experience reinforced two critical best practices: end‑to‑end testing across every purchase path (including renewals and special‑event flows) and having vendor support on standby during major launches.

Address Verification System (AVS): Validating Billing Information 

While 3D Secure focuses on authenticating the cardholder, AVS verifies that the billing address matches the issuer’s records. We rolled out AVS checks to every payment flow—from ticket purchases to quick‑donate widgets to membership renewals—offering organizations the choice between a ZIP‑only check or a full street‑and‑ZIP check. Early in QA, we discovered that the system validates based on numeric street components. To minimize confusion, we enhanced our checkout UI with clear prompts and optional visual reminders, ensuring patrons input addresses that align precisely with bank records. 

Once live, AVS quickly proved its value: mismatched addresses were flagged immediately, reducing the volume of fraudulent orders and easing chargeback disputes. Support teams reported a marked decrease in address‑related inquiries, allowing them to focus on more complex customer needs.

Verisoul 

Verisoul adds an AI layer of behavioral and network-based analysis to identify and block suspicious users across the site. By using large amounts of data AI can minimize fraudulent behavior not just at checkout, but earlier in the user journeys, such as during registration or login. 

We’ve deployed Verisoul on several core site areas, including registration, login, donation, and payment pages. It supports CMS-configurable settings that allow clients to: 

While Verisoul has been effective at reducing fake or risky accounts, there are a few caveats. For example, blocking based on shared network behavior can result in false positives, although clients have control to override or update these decisions. As with many security platforms, development costs can grow as feature use expands, but its flexibility and documentation make it a strong addition to any fraud-prevention toolkit.

TruValidate gives teams direct control over which users can log in or proceed to checkout, based on customizable rules tied to behavior or location. The tool tracks IP addresses to prevent fraudsters from creating new accounts, and can flag activity associated with bots, proxies, or suspicious patterns. 

Clients have used TruValidate to define targeted restrictions—for example, blocking logins from specific regions or stopping checkout when interaction patterns indicate likely fraud. Post-launch, organizations saw a measurable reduction in fraudulent purchases, particularly during high-traffic or high-value events.

Summary