Stop Fraud Without Stopping Commerce: Rethinking Identity Confidence in E-Commerce

by Laura Burrows 4 min read November 5, 2026

At A Glance

Digital commerce is creating more ways to shop and more ways for fraud to hide. Discover how you can effectively prevent e-commerce fraud.

Consumers are embracing marketplaces and artificial intelligence (AI)-assisted shopping while continuing to expect secure, seamless experiences. For e-commerce fraud teams, that raises a harder question: how do you recognize legitimate customers, transactions and AI agents as the identities and behaviors behind a purchase become more complex?

Experian’s 2026 U.S. Identity and Fraud survey of 2,075 consumers and 311 businesses points to a changing landscape. Third-party marketplace use is growing; 31% of consumers say they have already used AI to make a purchase or booking on their behalf, and security and privacy remain consumers’ top concerns in online experiences.

Fraud prevention must protect the experience, too

Consumers want protection, but they do not want checkout to slow down. Fifteen percent say they will abandon an online cart if a transaction takes longer than 30 seconds, and another 34% say they give up within a minute. Adding the same challenge to every shopper can cost conversions, while weak controls leave room for fake accounts, account takeover and payment fraud.

Four moments where identity confidence matters

  1. Account creation: Is this a legitimate identity?
    Fake accounts can become a foothold for downstream fraud. At account creation and guest checkout, email signals such as age, activity, popularity, validation status and domain risk can help identify suspicious patterns, including address variations used to create multiple accounts. The aim is to surface risks early without requiring every new customer to complete a more rigorous verification process.
  2. Login and session behavior: Does this interaction look trustworthy?
    A legitimate account can still be compromised, and automated or malicious activity may become a through session behavior. Behavioral analytics can assess activity during login, account opening and checkout using passive in-session signals. In Experian research, 83% of consumers said they feel safe using behavioral biometrics, the highest of any solution type, while only 7% of digital-only retailers reported using the method at account opening.
  3. Checkout: Do identity and payment signals align?
    When a transaction is submitted, account ownership verification can help confirm that the identity and payment details belong to the same account. Matching signals such as name, address, email, date of birth and phone against trusted records can help detect stolen card use and reduce false positives without adding steps for the shopper.
  4. Agentic commerce: Is the agent acting for a genuine customer?
    AI agents introduce a new trust question: whether the agent is authorized and acting on behalf of a real customer. Experian® Agent Trust verifies AI agents by comparing identity and payment credentials against those of verified, user-bound agents. Yet adoption of Know Your Agent tools remains low: 3% of digital-only retailers and 7% of click-and-mortar retailers report having adopted them.
e-commerce case study

Case study: Catch fake accounts at creation
An e-commerce company facing rising fake-account activity and chargebacks added Experian AtData Fraud Prevention Application Programming Interface (API) to its registration flow.

Within weeks, it reported lower fraud rates, fewer account-creation attempts and chargebacks, increased sales conversions and less friction for shoppers not flagged as at risk.

Recognize good commerce sooner

Effective e-commerce fraud prevention is not only about blocking risky activity. It is also about recognizing legitimate customers and transactions with enough confidence to allow them to proceed. By layering passive behavioral signals, identity, payment and account ownership verification, and agent trust across the journey, retailers can target review where it is needed and support a secure, low-friction experience.

Build identity confidence across the digital commerce journey

Explore how we help retailers detect risk before a transaction is submitted, verify identity and ownership of payments and prepare for trusted agentic commerce.

Unless attributed otherwise, data in this report comes from Experian’s 2026 U.S. Identity and Fraud survey, which includes responses from 2,075 U.S. consumers and 311 businesses (including 60 digital-only and click-and-mortar retailers). For an industry-agnostic breakdown of this year’s findings, see Experian’s 2026 U.S. Identity and Fraud Report.


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One Platform, Fewer Handoffs, Faster Decisions

The average credit union manages between 100-300 vendor relationships, and nearly a third have no dedicated staff managing them. What was supposed to drive efficiency is stunting growth and compromising the member experience. Application volume is continuing to rise, and consumers expect a loan decision at digital speed. But for credit unions, meeting that expectation isn't the only goal. They must also continue to make the right lending decisions while also prioritizing risk management. The institutions pulling ahead aren't the ones with the most tools. They're the ones that have turned credit, fraud and decisioning into a single, coordinated system. A single loan decision touches: Credit data Identity verification Fraud signals Decisioning logic Reporting Rarely does all of that live in one place. More often, it’s stitched together across separate platforms, each with its own contract and its own support line. When a credit view and a fraud view are generated separately, they must be reconciled instead of read together in the same instant. Every handoff is a place where a decision slows down and a signal gets missed. It's why a fraud pattern that would be obvious next to the credit file often surfaces only after the funds are already gone. Fragmentation also makes it harder to optimize lending strategies over time. When data, analytics and decisioning are disconnected, credit unions have less visibility into what's working, what's changing and where strategies need to be adjusted. An integrated decisioning ecosystem closes those seams by design, with credit, fraud and lending strategy running on one platform and accessed through a single Application Programming Interface (API). Identity and compliance capabilities can be an integrated decisioning flow. The data doesn't need to be reconciled after the fact because it was never separated in the first place. Stop overengineering, start removing friction When decisioning underperforms, the instinct is to build something more custom: a proprietary model, tighter segmentation, more configuration.  Custom modeling has its place for institutions with the scale and data to justify it. But often, for small or mid-sized credit unions, the bigger opportunity is a flexible decisioning strategy that uses a strong, continuously refreshed model. This shouldn't be either/or. The right approach gives credit unions a strong decisioning strategy today, with the ability to introduce more customized analytics and modeling as their needs evolve.  The same principle applies to strategy management: credit unions can gain greater control through self-service capabilities as they build a more sophisticated data science team.   That kind of flexibility is important as credit unions modernize their decisioning infrastructure. Research surveying more than 2,000 senior credit risk and fraud decision-makers across 11 countries found that organizations investing in unified platforms and modern infrastructure are moving faster and realizing the greatest Return on Investment (ROI).   As their needs evolve, credit unions can adjust decisioning strategies without introducing another layer of technology or relying on third parties to make every change. Until self-service is the right fit, choose a partner that understands what makes your credit union’s membership and risk appetite different from others.  Same member, same afternoon, faster answer Consider a credit union growing its auto and personal loan originations.  A member applies for an auto loan on Saturday afternoon. Credit data, identity and fraud signals are read together in a single real-time pass instead of separate reviews:  Most applications resolve immediately, approved or declined based on a more comprehensive view of the applicant. A smaller number are flagged for quick step-up verification and cleared within minutes. Only genuinely ambiguous cases reach an underwriter, who is working from one complete picture instead of reconciling multiple signals. The member has an answer before the weekend is over. The credit union books the loan before competitors even open on Monday. Built to scale This kind of process holds up today. However, the better test is whether it holds up eighteen months from now when application volume has grown, new products are live and the data flowing through the system looks nothing like it does right now. Future-proofing isn't only about handling more volume. It's about having room to be nimbler and make adjustments as quickly as the market changes. A credit union that relies on standard models within a decisioning strategy may want more customized analytics and greater control over its strategies tomorrow. Connect with us With more than three decades of credit expertise and one of the industry's largest data footprints, Experian helps credit unions do more than solve this year's friction. We're helping to build a decisioning foundation that scales, so growth becomes something the system absorbs, rather than something it must be rebuilt around. Explore More

October 21, 2026 by Zohreen Ismail

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