Riskified Adds Identity Risk Intelligence to Zendesk for Returns
Riskified has integrated its identity risk intelligence directly into Zendesk, letting support agents see returns-fraud signals in real time without leaving their normal workflow.
Riskified has integrated its identity risk intelligence into Zendesk, giving retailers a way to flag suspicious returns activity directly inside the customer service platform agents already use. The move connects Riskified's fraud-detection signals to the support workflow, so a customer service rep handling a returns request can see risk context without switching systems or escalating every case to a separate fraud team.
The integration is aimed squarely at returns fraud — a persistent loss category for retailers that has historically forced a trade-off between tightening controls and keeping the return process fast and frictionless for legitimate shoppers. By embedding risk scoring into Zendesk, Riskified is positioning the tool as a way to intercept bad-faith returns at the point of contact rather than after the fact.
Returns fraud sits at an uncomfortable intersection of loss prevention and customer experience: overly aggressive fraud rules punish honest customers with denied refunds and slow, adversarial support interactions, while lax controls invite abuse. Bringing identity risk intelligence into the same interface agents use for everyday tickets means fraud signals can inform a conversation in real time, rather than triggering a blanket policy that treats every customer as a suspect.
For retail and CX leaders, this reflects a broader shift: fraud and risk tooling is increasingly being designed to live inside front-line service platforms rather than in back-office systems analysts check separately. That has implications beyond returns — it's a template for how identity and risk data can be operationalized at the point of service without adding steps for either the customer or the agent.
The real story here isn't fraud prevention — it's where the decision gets made. Moving risk intelligence into the agent's existing screen is a service-design choice as much as a security one.
Most fraud tools fail customers not because they're wrong, but because they're invisible to the person actually talking to the customer — so agents either override them blindly or enforce them robotically, and both erode trust. The fix isn't smarter fraud models; it's giving the human in the loop context they can act on with judgment. Retailers that treat this as a friction-removal project, not just a loss-prevention one, will see fewer angry legitimate customers and fewer fraudulent returns slipping through on autopilot.