{"id":77634,"date":"2026-01-30T05:33:19","date_gmt":"2026-01-30T05:33:19","guid":{"rendered":"https:\/\/dralysstore.com\/blog\/?p=77634"},"modified":"2026-01-30T05:33:19","modified_gmt":"2026-01-30T05:33:19","slug":"agentic-shopping-is-creating-a-zero-signal-fraud-crisis","status":"publish","type":"post","link":"https:\/\/dralysstore.com\/blog\/agentic-shopping-is-creating-a-zero-signal-fraud-crisis\/","title":{"rendered":"Agentic Shopping Is Creating a Zero-Signal Fraud Crisis"},"content":{"rendered":"
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As AI shopping agents remove friction from online commerce, they are also erasing the behavioral signals retailers rely on to detect fraud \u2014 creating a visibility gap that could trigger the largest friendly-fraud wave since e-commerce began.<\/p>\n

According to Kevin King, VP of credit risk at LexisNexis Risk Solutions<\/a>, friendly fraud is the fastest-growing threat retailers do not see coming. Agentic shopping is turning that blind spot into a prevention nightmare.<\/p>\n

Also known as first-party misuse, friendly fraud has become a global crisis, now accounting for approximately 75% of all chargebacks and costing merchants an estimated $132 billion annually. Agentic shopping is becoming a black box that could push these numbers even higher.<\/p>\n

\u201cThis moment mirrors the early days of e-commerce when fraud outpaced security,\u201d Conrad Kennington, VP of AI at digital risk management platform firm Accertify<\/a>, told the E-Commerce Times.<\/p>\n

Friendly Fraud Drives a Two-Fold Retail Crisis<\/h3>\n

Some commerce experts claim that customer fraud, in which customers dispute legitimate transactions, is now the top global fraud category. Others urge digital security teams to reassess what prevention actually looks like, leveraging identity insights, behavioral analytics, stronger dispute evidence, and smarter issuer\u2013merchant collaboration.<\/p>\n

E-commerce experts suggest that the liability chain for those disputes is entirely unclear. The bigger issue is data loss. Each stage of digital commerce has stripped away fraud signals. Agentic transactions reduce that visibility to near zero.<\/p>\n

However, Kennington noted that if purchases are made using one-time tokens with no behavioral context, fraud teams could be left to assess risk based on a single data point: the shipping address.<\/p>\n

He explained that for friendly fraud, historical context is critical. A customer with years of clean behavior then disputes a single purchase and subsequently returns to normal behavior, likely made an accidental agent- or child-triggered purchase.<\/p>\n

\u201cA customer whose behavior turns bad and stays bad is more indicative of intentional first-party abuse. First-time offenders remain the hardest to detect because there is no behavioral baseline yet,\u201d he said.<\/p>\n

Why Agentic Shopping Leaves No Fraud Trail<\/h3>\n

Unlike human shoppers or fraudsters, AI agents will make purchases with almost no footprint. AI shopping assistants need no browser data, no device signals, no form fields, and often no memory from the consumer who delegated the task.<\/p>\n

Security experts warn that this autonomous freedom opens the door for both large-scale abuse by fraudsters and a wave of friendly fraud when people dispute charges made by agents they forgot about or that a child accidentally activated.<\/p>\n

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Kennington reasoned that retailers need a world in which autonomous agents must authenticate themselves before transacting. He countered that this problem is largely solved for humans.<\/p>\n

\u201cWe now authenticate ourselves via multifactor authentication and passkeys, as an example. The agent ecosystem needs to catch up to that same standard,\u201d he urged.<\/p>\n

How Identity Data Replaces Lost Fraud Signals<\/h3>\n

According to Andy Mortland, VP of product and development at Accertify, autonomous agents reduce traditional telemetry. Still, merchants can gain meaningful, actionable identity insights.<\/p>\n

\u201cThe order form itself continues to carry valuable data: payer name, email, payment method, SKU patterns, and any information tied to delivery or fulfillment. Those data points help provide context to assess whether a transaction reflects legitimate purchasing behavior or elevated fraud risk,\u201d he told the E-Commerce Times.<\/p>\n

In his view, historical context remains one of the strongest indicators of a transaction\u2019s legitimacy. A shopper\u2019s long-term reputation, including purchase history, dispute behavior, fulfillment outcomes, and return or refund patterns, is highly predictive and is unaffected by the disappearance of browser-level signals.<\/p>\n

Additionally, Accertify can already distinguish between bots and humans, so that is not the issue, Kennington added. The real question is how to distinguish good bots from bad bots.<\/p>\n

\u201cIn this context, behavioral analytics is less about page scroll gestures and more about long-term historical transaction patterns such as prior buying patterns, average spend, category consistency, and dispute outcomes rather than session-level behavioral signals,\u201d he said.<\/p>\n

For example, if someone who reliably shops low- to mid-priced retail suddenly purchases a luxury watch, that anomaly matters regardless of whether a human or an agent initiated the transaction.<\/p>\n

Why Legacy Fraud Tools Miss Friendly Fraud<\/h3>\n

According to Jeffrey Feinstein, global head of data science at LexisNexis Risk Solutions, fraud focus has historically been on third-party types, which are easier to predict due to identity anomalies. For fraudsters to succeed, they need to control at least one element of an identity \u2014 like a phone number, email address, device, or address.<\/p>\n

\u201cThis creates an identity change signal that is easier for lenders to prevent with notifications and alerts, such as a notice to the consumer of an address change, which is easier for data providers to identify based on a data signal,\u201d he told the E-Commerce Times.<\/p>\n

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By comparison, first-party fraud comes in at least three varieties, each with its own warning flags. First, consumers are engaging in fraudulent transactions under their own identities. The second type is synthetic identities created by another person to commit first-party fraud in the synthetic identity\u2019s name. Third is a scam or mule behavior, in which a third party convinces consumers to engage in a transaction that is not in their best interest.<\/p>\n

Feinstein observed that cybersecurity teams often do not segment fraud so cleanly into categories such as first-, third-, and synthetic fraud. They do not want to create a walled-in set of criteria.<\/p>\n

\u201cFraudsters don\u2019t specialize in specific tactics. They think creatively about how to steal funds from institutions. They use a variety of tools to evade classification,\u201d he offered, adding that focusing on anomalies using broader definitions, in addition to<\/em> classic definitions, can often classify fraud more completely.<\/p>\n

The Fraud Risks No One Will Quantify<\/h3>\n

Feinstein deflected from discussing how fraudulent disputes contaminate the financial system beyond the merchant. Clearly, the steady rise in consumer fraud impacts credit files, issuer risk models, and lending decisions. This risk is still massively underpriced.<\/p>\n

\u201cThis is a dangerous question because if I answer it, I\u2019d be informing fraudsters on how to commit fraud,\u201d he replied.<\/p>\n

Feinstein added that information gaps ultimately can be empowering to fraudsters. That is why LexisNexis Risk Solutions encourages its customers to provide as much information as possible to corroborate transaction information.<\/p>\n

When Shoppers Forget Their AI Bought Something<\/h3>\n

Retail experts predict a wave of disputes from consumers who forgot they authorized an agent to make a purchase. That will require new forms of compelling evidence to prove a consumer delegated a task to an AI.<\/p>\n

Accertify\u2019s Kennington agreed with that viewpoint. Multiple groups are developing protocols to get payment information, he revealed. But most are not yet considering the implications of fraud risk.<\/p>\n

He emphasized that payment networks will eventually have to agree on a single protocol to match merchants with buyers. For example, with a single-use token, if there is a dispute, the token can show that the person tasked the agent with making the purchase.<\/p>\n

\u201cFor Accertify, it is important that we actively engage with industry groups to help shape what these new evidence standards will look like,\u201d he said.<\/p>\n

Mark Michelon, Accertify\u2019s president, further confirmed that his company\u2019s technology is helping address fraud issues.<\/p>\n

\u201cAccertify\u2019s consortium scale data helps give merchants the ability to detect the truly fraudulent or abusive so they can target interventions precisely and avoid those draconian, trust-damaging rules,\u201d he told the E-Commerce Times.<\/p>\n<\/p><\/div>\n

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Published by Dralys Blog \u2013 Stories | Insights | Innovation<\/strong><\/p>\n

Discover more on DralysStore.com<\/a><\/p>\n

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