For online retail, fraud is becoming more expensive and difficult to manage. According to Juniper Research, global eCommerce fraud losses are projected to rise from US $56 billion to more than US $131 billion by 2030. For merchants, the cost is not limited to the fraudulent order itself. Every "fraud dollar" averages $4.61 in total fees and losses when accounting for chargeback fees, refund costs, fulfillment waste, manual review, and lost legitimate revenue.

For online retailers, AI can help teams evaluate order, account, payment, device, and behavioral signals in real time to identify high-risk activity earlier, reduce false declines, lower manual review workload, and trigger the right operational response. Yet, over 40% of merchants still rely on manual processes to prevent fraud.

In this article, we look at the main fraud risks affecting eCommerce brands, where AI improves fraud prevention, what data and integrations make it work, and how AI fraud agents can move brands from reactive blocking toward controlled and proactive response automation.

TMO helps Magento and Shopify brands implement fraud detection solutions including risk scoring, escalation rules, and response automation. Request a guided demo to see how AI can help reduce fraud losses, lower manual review effort, and protect legitimate revenue.

Why Fraud Risk Gets Harder as eCommerce Brands Scale

Fraud risk changes as eCommerce brands grow. A simple rule-based setup may work when order volume is low, markets are limited, and fulfillment flows are straightforward. It becomes less reliable when brands expand across more regions, payment methods, customer segments, promotions, and fulfillment models.

There are four main competing pressures:

1. Chargebacks eating into margins: These create direct revenue loss, fees, operational workload, and potential payment processor penalties. Friendly fraud, which makes up over 60% of chargebacks, makes this harder because the original transaction may look legitimate.
2. Faster-changing fraud tactics: Fraudsters use bots, synthetic identities, promo abuse, account takeover, and increasingly convincing AI-generated damage photos to bypass systems. Fixed thresholds need constant manual adjustment and often struggle to keep up.
3. Region-specific fraud patterns: Payment behavior, refund abuse, delivery risk, and customer verification expectations vary by market. A single global fraud threshold may block too many legitimate orders in one region while missing risk in another.
4. Manual review costs: Manual review can fill gaps in early fraud controls, but it scales poorly as order volume grows. More orders usually mean more reviewers, slower fulfillment, and higher operating cost.

The opportunity for AI is to make fraud decisions more granular. Instead of relying only on fixed rules, AI can evaluate order, payment, device, account, and behavioral signals together, helping teams identify high-risk activity while reducing unnecessary friction for legitimate customers.

Common Types of eCommerce Fraud

eCommerce fraud is not limited to stolen credit cards. It can appear across payment, account, promotion, refund, and fulfillment workflows. As brands scale across markets and sales channels, prevention becomes harder when local platform rules, payment norms, and dispute workflows vary by market.

An effective setup needs to connect store data, payment gateways, customer records, fulfillment workflows, and escalation rules. This is where TMO can support with AI-ready commerce architecture, fraud tool integration, and controlled response automation.

Fraud Type	Common Tactics	Impact
Payment fraud (CNP fraud)	Stolen card data, card testing attacks, suspicious high-velocity transactions.	Card-not-present fraud accounts for 65–73% of global card fraud losses, the largest single category of eCommerce fraud.
Friendly fraud	Chargebacks filed after delivery, false "item not received" claims, or abuse of refund policies.	First-party fraud made up 36% of global fraud incidents in 2024.
Account takeover (ATO)	Credential stuffing, phishing logins, synthetic identity registratio.	ATO fraud losses projected at US $17B in 2025, up from $13B in 2024.
Bot attacks and promo abuse	Bulk fake orders, gift card cash-out schemes, promo code abuse.	Bot traffic accounts for over 50% of global internet traffic, with scrapers and automations becoming increasingly accessible.

Where AI Adds Value in Fraud Prevention

For online retailers, the a big challenge is balancing risk control with revenue protection. This is why increasing or making fraud rules stricter is not always the answer: if controls are too loose, the brand absorbs fraud losses, but if they are too tight, legitimate customers are declined, delayed, or pushed through unnecessary verification steps.

AI helps reduce this tradeoff by evaluating more signals in real time. Instead of relying only on fixed thresholds, smart systems can assess order value, device behavior, payment data, account history, transaction velocity, shipping patterns, and customer behavior together to generate a more granular risk score.

1) Reduce Fraud Losses and Chargeback Exposure

AI models evaluate dozens to hundreds of behavioral signals at once (device fingerprinting, IP geolocation, transaction velocity, purchase patterns, account history) to generate a dynamic risk score.

For example, a high-value order from a new account may not be risky on its own. But if it also uses a suspicious device, mismatched billing and shipping details, a recently changed address, and unusually fast checkout behavior, the risk profile changes.

Harry’s, a US men’s grooming D2C brand, used AI fraud detection to address promo abuse, payment fraud, and account abuse. The system combined risk scoring with account network analysis to assess transaction risk in real time and support an approve, review, or decline workflow. Within two months of launch, Harry's chargeback rate dropped by 85%, with the dedicated fraud team remaining a single person even as order volume grew.

2) Lower False Declines and Recover Legitimate Revenue

Behavioral biometrics, navigation patterns, mouse movement, and form-filling behavior are micro-signals that are extremely difficult for fraudsters to replicate at scale. AI uses these signals to tell the difference between a returning customer on a new device and a fraudster using a spoofed one, approving more legitimate orders without extra verification steps. False decline rates can drop by up to 30%.

If you'd like to learn how AI can help your store protect margins and lower fraud management costs, contact our team to request a demo of our AI modules for Magento stores.

3) Reduce Manual Review Workload

AI fraud systems can automatically clear the vast majority of low-risk orders, routing only complex or high-risk transactions to human reviewers.

As OpenTable expanded into digital gift cards, it adopted an AI fraud detection solution to automate a process that previously relied on manual review. Within a year, OpenTable cut its manual review rate from 30% to 5%, reducing manual review workload by roughly 80%, while fraud detection accuracy improved 200%.

How to Enable an AI Fraud Detection Model

For an intelligent fraud detection model to work effectively, it needs access to the right data to score risk accurately and connect to the right workflows to act on that risk.

Before evaluating tools or implementation paths, assess whether your commerce stack communicates across four layers:

1) Order and Account History

An anti-fraud system needs more than the current transaction. It should be able to evaluate customer history, past orders, dispute records, account changes, payment methods, address updates, and login activity. This usually means your storefront, CRM, and payment gateway need to share data on the same timeline

If this data is incomplete or delayed, the model works with a partial view. A suspicious order may look normal because prior risk signals are missing. A legitimate customer may look risky because their account history is not connected in time.

2) Device and Behavioral Signals

Device fingerprinting, IP geolocation, login patterns, checkout behavior, session velocity, and behavioral biometrics help identify account takeover, card testing, bot activity, and synthetic identity patterns.

These signals usually come from a dedicated fraud tool, SDK, or storefront-level integration, not from the commerce platform alone.

3) Escalation Rules

While AI fraud detection usually produces a risk score or recommendation, the business still needs to define what happens next.

• Low-risk orders may be approved automatically
• Medium-risk orders may require additional verification
• High-risk orders may be held for review
• Confirmed fraud may trigger account restrictions, fulfillment holds, refund pauses, or payment action.

Without clear escalation rules, teams either over-rely on raw risk scores or route too many cases back into manual review.

4) Cross-System Response Workflows

Taking action on a flagged order (holding fulfillment, freezing an account, triggering a refund) requires integration between your fraud tool and your OMS, CRM, and fulfillment systems. Without this, fraud detection stays a reporting function instead of an operational one.

This is the same foundation that supports other AI use cases like product recommendations or agentic customer service. If you've already invested in this for another use case, fraud detection can usually plug into it.

AI Agents: From Risk Detection to Controlled Response Automation

Once the right data and workflows are connected, AI fraud detection can move beyond transaction-level scoring. AI fraud agents can monitor risk signals across accounts, orders, devices, payment behavior, and fulfillment workflows, then trigger predefined actions when risk patterns meet specific thresholds.

This does not mean the AI should make unrestricted fraud decisions. In practice, most effective fraud agents operate within clear business rules: what they can flag, what they can hold, what they can escalate, and which actions require human confirmation:

• If an account changes its shipping address repeatedly within a short period, the agent can escalate future orders from that account for review.
• If an IP range shows a spike in bulk account registrations, the agent can flag related accounts and restrict high-risk actions.
• If chargeback rates rise unexpectedly for a specific product category, the agent can trigger secondary verification for orders in that category.
• If a high-risk order is detected, the agent can notify the fraud team, apply a fulfillment hold, update the customer risk label in the CRM, and route the case for manual review.

For higher-risk actions, such as refunds, account suspension, payment reversal, or permanent customer restrictions, human oversight should remain part of the workflow. An AI agent can flag the order for review, notify the fraud team, send a hold instruction to fulfillment, update the account's risk label in the CRM, and trigger a refund if no manual confirmation is received within a set window. It is worth noting that much of what looks like autonomous response is really predefined rules triggering automatically rather than the model making independent judgment calls, and that distinction matters when setting expectations internally.

A note on agentic commerce: as more transactions involve AI shopping agents acting on behalf of customers, fraud systems will also need to evolve. Current fraud controls often rely on device behavior, session activity, and user interaction patterns. Distinguishing a legitimate customer-authorized shopping agent from a malicious bot is likely to become a more important fraud prevention challenge.

Build the Foundations for AI Fraud Detection with TMO

TMO specializes in building AI-ready commerce stacks by designing the technical layer that ensures your commerce platform has the data, event tracking, and system structure needed to support reliable AI features, including fraud detection. 

TMO helps Magento and Shopify brands implement AI solutions across real eCommerce workflows. This can include fraud tool integration, platform and payment data connection, review workflow design, fulfillment hold logic, and custom AI agent workflows for controlled fraud response.

If your brand is dealing with rising chargebacks, false-decline risk, account abuse, promo abuse, or growing manual review costs, the first step is usually to assess whether the current commerce stack is ready to support AI-driven fraud detection.

Request a guided demo to see how AI can connect with your store data, platform architecture, and operational processes to reduce fraud losses, lower manual review effort, and protect legitimate revenue.

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