Opayo + ACI Fraud Management: Case Study, Features, and Results

• 11.08.2026
• Blog
• Ethan Martinez
• 0

For online merchants, payment acceptance and fraud control are no longer separate operational concerns. A smooth checkout must be matched by reliable risk screening, especially as fraud tactics become more automated, cross-border, and data-driven. The combination of Opayo payment processing and ACI Fraud Management offers a practical model for reducing fraud losses while protecting legitimate revenue.

Why Fraud Management Matters in the Opayo Payment Flow

Opayo, formerly known as Sage Pay, is widely used by UK and European businesses to accept card payments online, over the phone, and in person. It is valued for its familiar merchant tools, secure payment pages, and support for established payment workflows. However, payment processing alone does not solve the full fraud problem. A transaction may be technically valid, authenticated, and authorised by the issuer, yet still result in a chargeback if the cardholder later disputes it.

This is where ACI Fraud Management becomes relevant. Rather than treating every transaction the same, it evaluates risk signals in real time. These signals can include transaction value, customer history, device behaviour, location, velocity, delivery address, card attributes, and known fraud patterns. The objective is not only to block fraud, but also to avoid rejecting good customers.

The most effective fraud strategy is balanced: reduce confirmed fraud, minimise manual workload, and preserve conversion at checkout.

Case Study: Mid-Market Ecommerce Retailer

Consider a representative case based on a mid-market UK ecommerce retailer selling consumer electronics and home technology. The company used Opayo for online card payments and had experienced a steady increase in fraud attempts during peak sales periods. The most common problems included account takeover, stolen card use, and orders sent to freight forwarding or temporary addresses.

Before implementing ACI Fraud Management alongside Opayo, the merchant relied heavily on basic rules and manual checks. Any order above a certain value was reviewed by the operations team, regardless of the customer’s history or risk profile. This approach created three issues:

• High manual review volume: staff reviewed too many legitimate orders.
• Delayed fulfilment: customers experienced slower dispatch for high-value purchases.
• Inconsistent decisions: fraud analysts applied different judgement depending on workload and available information.

The retailer needed a more structured system that could integrate into the payment journey without creating unnecessary friction. The chosen model used Opayo to manage payment acceptance and ACI Fraud Management to assess transaction risk before fulfilment decisions were made.

Implementation Approach

The implementation began with a review of historical fraud, chargebacks, refunds, and false positives. This step was important because fraud tools are only effective when tuned to the merchant’s real trading patterns. For example, a high-value order may be normal for a business selling premium electronics, but unusual for a merchant selling low-cost accessories.

The retailer then configured fraud rules and scoring thresholds. Transactions were grouped into three decision paths:

1. Approve: low-risk orders moved directly to fulfilment.
2. Review: medium-risk orders were sent to a fraud analyst or customer service team.
3. Decline or hold: high-risk orders were blocked, delayed, or subjected to additional verification.

Importantly, the system did not rely on one simple rule, such as “decline all orders above £500.” Instead, it combined multiple signals. A returning customer using the same device and delivery address might pass easily, while a first-time customer using a new device, mismatched address details, and rapid repeated checkout attempts would receive a higher risk score.

Key Features Used

The most valuable features in the Opayo and ACI Fraud Management setup were those that improved decision quality without slowing down every customer. Several capabilities stood out.

• Real-time fraud scoring: each transaction received a risk score before the order was released for fulfilment.
• Custom rules engine: the merchant could create business-specific controls for product type, basket value, country, delivery method, and customer history.
• Velocity checks: repeated attempts using the same card, email, IP address, or device could be flagged quickly.
• Manual review queues: suspicious but not clearly fraudulent orders were prioritised for analyst review.
• Positive and negative lists: known trusted customers could be treated differently from previously abusive accounts or addresses.
• Reporting and analytics: fraud trends, rule performance, chargeback outcomes, and review efficiency were monitored over time.

One of the most important operational improvements was the ability to refine rules after observing performance. If a rule created too many false positives, it could be adjusted. If a fraud pattern emerged around a specific product category or shipping option, a new rule could be introduced quickly. This gave the retailer a more responsive defence than static checkout controls.

Results and Business Impact

After three months, the retailer reported measurable improvements across fraud prevention and operations. Chargebacks decreased by 38%, while the value of confirmed fraudulent orders fell by 42%. At the same time, the approval rate remained broadly stable, with less than a 1.5 percentage point change. This was significant because aggressive fraud prevention often reduces fraud by rejecting too many legitimate customers.

The manual review rate also improved. Before the project, approximately 18% of orders were sent for manual checks during high-risk periods. After tuning the ACI rules and scoring model, that figure fell to around 9%. The fraud team was able to focus on genuinely suspicious cases instead of reviewing large numbers of routine orders.

Customer experience improved as well. Legitimate returning buyers faced fewer delays, especially for higher-value products. In practical terms, the merchant could ship more orders on the same day because fewer transactions were waiting in review queues. The result was a more efficient operation, not merely a lower fraud rate.

Lessons from the Case

The case highlights several lessons for merchants considering Opayo with ACI Fraud Management. First, fraud tools should not be installed and left unchanged. They need ongoing monitoring, rule optimisation, and feedback from chargeback outcomes. Second, merchants should track both fraud reduction and false positives. A fraud strategy that blocks legitimate customers can be as damaging as fraud itself.

Third, collaboration between finance, ecommerce, customer service, and warehouse teams is essential. Fraud signals often appear across different departments. A customer service agent may notice unusual behaviour, while a fulfilment team may recognise suspicious delivery instructions. Feeding this knowledge into fraud rules makes the system stronger.

Conclusion

Opayo and ACI Fraud Management can form a strong payment and risk management combination for merchants that need dependable card acceptance and more sophisticated fraud control. Opayo supports the payment flow, while ACI adds the risk intelligence needed to distinguish between trusted customers, suspicious behaviour, and likely fraud.

The strongest results come from careful configuration, realistic thresholds, and continuous improvement. For merchants facing rising chargebacks, manual review pressure, or inconsistent fraud decisions, this type of setup can deliver measurable gains: fewer losses, faster fulfilment, and better protection for legitimate customers. In a serious ecommerce environment, that balance is the real measure of success.