Fraud decisions shouldn’t feel like a black box.
For years, merchants have had to rely on systems that spit out a score or “approve” and “decline” outcomes without explaining why. That lack of visibility makes it hard to know whether a flagged order was truly fraudulent or whether a loyal customer was mistakenly turned away.
Explainable AI (XAI) in ecommerce changes that. By surfacing risk categories and the signals contributing to each decision, XAI gives fraud teams the insight they need to trust that the decisions their fraud systems deliver are correct. For merchants, that means fewer support tickets, faster case resolution and higher conversion rates; for shoppers, it means a smoother buying experience. Let’s explore how.
TL;DR
- Explainable AI (XAI) moves beyond simple “approve/decline” decisions, showing the specific reasons behind each outcome. This visibility prevents false declines, saving both revenue and good customers.
- By revealing the factors behind a flag, XAI allows fraud teams to save good orders, meet compliance standards (like GDPR) and build team accountability.
- This approach helps businesses spot coordinated attacks and gives teams the actionable insights they need to quickly resolve cases.
- Ultimately, solutions like Signifyd’s Guaranteed Fraud Protection with its Explore, Investigate, Act feature make XAI practical, delivering transparency and control so you can turn fraud prevention from a challenge into a growth driver.
What is explainable AI in ecommerce?
Explainable AI makes automated fraud detection decisions in ecommerce easier for merchants to understand. Instead of a blunt “approve” or “decline,” explainable AI shows the red flags behind the outcome and details how much influence each one had.
That can shape the customer journey in practical ways.
- Eliminate customer insults: Once a merchant’s risk team has the reason for a decline they can determine whether mitigating circumstances override the worrisome signals.
- Provide transparency for shoppers: If a customer asks about a declined order, a merchant will be able to offer a reasonable explanation for the decline.
When it comes to fraud protection, explainable AI not only provides the answer, but also explains the reasoning. That context gives teams clear visibility into how decisions are being made, making it easy to explain outcomes to other team members, stakeholders and customers.
Why is explainable AI important?
Explainability matters because it protects revenue, maintains compliance and builds accountability.
Revenue
Fraud checks without explanation can be costly. When a legitimate customer’s order is declined with no explanation, both the sale and shopper are often lost. In fact, our 2025 State of Commerce Report shows that 35% of consumers either abandon the purchase or go to a competitor when faced with a false decline. Explainable AI flags the factors that triggered a decline so you can adjust rules or policies accordingly, preserving legitimate sales while still catching fraud.
Compliance
Privacy regulations around the world expect transparency in automated decisions. Under the General Data Protection Regulation (GDPR), for instance, people have the right to understand and contest decisions made solely by algorithms. Explainable AI equips merchants with audit-ready reasoning by showing how conclusions were reached, letting them log decisions, support human review and respond quickly to inquiries.
Accountability
Legacy, rules-based fraud systems often leave merchants guessing: Did the system stop a real threat or simply apply rigid, static rules to an honest order? Combining a machine learning system with explainable AI creates a transparent record of decision factors, which lets teams confirm whether outcomes align with their risk strategy, and adjust quickly if they don’t.
How explainable AI reduces fraud in ecommerce
Beyond influencing revenue, compliance and accountability, explainable AI has an even more direct impact: It helps fraud teams cut false declines, spot coordinated attacks and determine next steps after a decision has been made.
Cutting false declines and saving good orders
Legacy, rules-based systems and even machine learning fraud protection that delivers a score to merchants often leave them struggling to see why an order was declined. Was it a new device, a mismatched address or a purchase outside the shopper’s usual pattern? Explainable AI surfaces those details so fraud teams can spot when good customers are mistakenly flagged. With that visibility, they can approve more legitimate orders, protect revenue and strengthen long-term customer value.
Spotting coordinated fraud
Fraud often emerges as repeated attempts across different accounts, devices and merchants. In isolation, each transaction looks normal. But when viewed together, they reveal an otherwise hidden pattern. Explainable AI connects those dots across IPs, addresses or payment methods, giving fraud teams the insight they need to stop organized attacks early.
Guiding fraud teams
Fraud prevention works best when teams know what to do next. Explainability provides actionable context, showing when to verify an address, request documents or release an order.
How cutting-edge fraud protection provides explainable AI for ecommerce
Sophisticated merchants can no longer rely on AI-driven fraud protection that delivers only an approve or decline decision. They need explainable AI (XAI), meaning solutions that provide the key reasons behind the decision. Signifyd’s Staff Product Manager Celena Ng explains how future-focused fraud protection provides the insights a retailer needs to explore, investigate and act on the risk signals that AI surfaces.
In certain fraud prevention solutions, like Signifyd, with its Explore, Investigate, Act feature, those reasons appear in detail on a dashboard, helping analysts resolve cases faster and explain outcomes with confidence.
Benefits and challenges of XAI in ecommerce
Explainable AI offers significant benefits for ecommerce, but it’s important to be aware of the potential historical challenges.
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The trade-offs are real, but manageable. In fact, mitigating the historic challenges with explainable AI has become a differentiator in fraud protection. With the right balance of safeguards — i.e. role-based access, privacy-safe explanations and auditable logs — combined with innovative fraud solutions that prioritize transparency and control along with explainability, you can unlock the value of XAI while keeping both revenue and customers safe.
Three use cases for explainable AI in ecommerce
Explainable AI can be used in multiple areas of ecommerce, from reducing false declines to safeguarding against fraud. Here are a few real-world examples of it in action:
1. Lowering false declines at checkout
A loyal customer orders a high-value designer handbag, but the system flags it as risky giving significant weight to the fact that the device being used is new and unrecognized. The system highlights the signals behind the flag. Fraud analysts weigh them against the customer’s strong history and approve the order.
2. Stopping serial return fraud
A buyer with a clean transaction history orders the same dress in three sizes and two colors. They check out without any red flags. But after delivery, they return all but one item. This pattern repeats again a week later: the buyer orders the same jacket in different sizes . Explainable AI flags the buyer’s account for review — the multiple sizes and colors bought together signal bracketing, while the frequent returns mark the customer as a serial returner. The merchant adjusts its return conditions for that account to prevent future abuse.
3. Spotting account takeovers (ATOs)
A customer logs into their account from a new location on an unrecognized device, immediately changes their shipping address and places an order for three high-end tablets. The fraud system spots these risky signals, blocks the transaction from going through, locks the account and alerts the merchant’s fraud team with the reasoning behind its decision.
Taking control with explainable AI
As you can see, explainable AI offers more than just insight. It puts you in control. It turns approvals and declines into clear reasons and next steps, so your team can fine-tune risk thresholds, document policy and move from reactive blocks to proactive risk management — all while keeping good customers moving through checkout.
At Signifyd, we built our Commerce Protection Platform and Guaranteed Fraud Protection solution with explainability at their core. Our solution delivers the transparency and actionable insights you need to confidently optimize fraud decisions, protect revenue and turn fraud prevention from a challenge into a growth driver. See how.
FAQs
What is an example of explainable AI in ecommerce?
In ecommerce, explainable AI is used in fraud prevention systems that provide clear reasons behind order approvals or declines. Instead of a simple yes/no decision, these systems explain the factors influencing each decision, such as transaction history, device information or purchase patterns. This transparency helps merchants fine-tune fraud rules and improve the customer experience by reducing false declines while blocking fraud.
How can businesses use explainable AI?
Ecommerce businesses use explainable AI to gain transparent insights into AI-powered decisions, enabling them to make informed adjustments and improve outcomes. In short, explainable AI helps teams understand the ‘why’ behind automated recommendations.
How can ecommerce companies evaluate explainable AI solutions?
Ecommerce companies should evaluate explainable AI solutions based on how clearly they explain decision factors, the ease of integrating with existing systems and their impact on key metrics like fraud detection accuracy and false decline rates. Look for platforms that offer real-time, actionable insights and tools that allow customizing risk thresholds without adding friction for customers, like Signifyd’s Guaranteed Fraud Protection solution coupled with the transparency and control offered by Explore, Investigate, Act.
Want to see what explainable AI can do for you? Let’s talk.
