stripeJul 21, 2026

Analyzing the evidence that helps businesses win “product not received” disputes (opens in new tab)

“Product not received” disputes are Stripe’s most common nonfraud dispute category, and strong, specific fulfillment evidence is closely associated with higher win rates. Analysis of one million disputes found that physical-goods businesses benefited most from confirmed delivery data, while digital businesses benefited from usage records and processor-verified refunds. The findings suggest businesses should connect fulfillment systems to dispute workflows and submit evidence strategically. ## Delivery Evidence for Physical Goods - Delivery confirmation correlated with a **27 percentage point** higher win rate than disputes without it. - Adding a GPS delivery map increased the lift by another **15 percentage points**. - A recipient signature added a further **2 percentage points**. - Disputes containing all three forms of evidence had a **44 percentage point** higher win rate. - Many businesses fail to provide this evidence because shipping and dispute systems are disconnected, making order matching a manual, difficult-to-scale process. ## Timing of Tracking Evidence - A tracking number is much more persuasive once it shows confirmed delivery. - Evidence submitted after delivery was confirmed correlated with a **27 percentage point** higher win rate. - Evidence submitted while a package was still in transit produced only a **2 percentage point** lift. - If the response window allows, businesses should wait for delivery confirmation. - If early submission is necessary, they should document that the shipment remains within the delivery timeframe agreed to at checkout. ## Evidence for Digital Goods - Digital activity and usage logs—such as records showing that a customer streamed, downloaded, or accessed the purchased product—correlated with a **10 percentage point** higher win rate. - Service documentation, including provisioning records, produced an **8 percentage point** lift. - Specific evidence of consuming the purchased content is stronger than general proof that the customer had access to the service. ## Refund Verification - For digital-goods businesses, evidence of a full refund processed through Stripe correlated with a **63 percentage point** higher win rate. - Refunds issued through other channels, such as store credit, produced only a **6 percentage point** lift. - Processor-issued refunds are more verifiable to card issuers because they leave a record on the card network. ## Stripe’s Automated Approach - Stripe’s Smart Disputes can automatically assemble evidence packets using shipping and fulfillment data. - Businesses can provide a carrier and tracking number, after which Stripe retrieves delivery status, timestamps, and location information from supported providers. - Additional communications or documentation can be combined with the generated packet. - If no action is taken before the deadline, Smart Disputes can submit the response automatically. Businesses should prioritize specific, independently verifiable fulfillment evidence and coordinate submission timing with the actual delivery or usage status.:VEVENT

dispute-managementaistripepayment-processing+3

stripeMay 27, 2026

Expanding Stripe Radar to protect more of your business (opens in new tab)

Stripe has significantly expanded Radar from card fraud prevention into a broader, AI-powered risk platform. It now protects transactions across global payment methods, supports off-Stripe fraud signals and custom models, detects newer abuses such as multi-account and pay-as-you-go fraud, and helps platforms assess merchant risk. Stripe’s goal is to let businesses intervene earlier and with greater precision while reducing false positives and operational losses. ## Global Payment Coverage and Custom Fraud Models - Radar now protects transactions across supported payment methods, including: - Bank debits - BNPL - Crypto - Digital wallets - Real-time payments - Cash vouchers - Fraud signals such as IP addresses and device fingerprints can now protect transactions across payment methods and businesses on the Stripe network. - Stripe reported a 71% reduction in suspected fraud over five months for businesses using Affirm, Cash App, Klarna, and PayPal. - New multiprocessor signals predict: - Whether a transaction may trigger an early fraud warning - Whether it is likely to result in a fraudulent dispute - Businesses can use these predictions to refund transactions early, gather evidence, or adjust dispute strategies. - Custom fraud models allow businesses to provide proprietary signals such as: - Product catalog information - Loyalty status - Behavioral data - Structured metadata - Early adopters detected at least 15% more fraud without increasing false positives. ## Defending Against New Fraud Types ### Multi-Account Abuse - Fraudsters create multiple accounts to reuse promotions or distribute stolen-card activity. - More than one in six AI-company sign-ups on Stripe are associated with multi-account abuse. - Radar evaluates accounts in real time using network-wide signals such as device fingerprints, IP addresses, and email domains. - ElevenLabs reportedly blocks around 2,000 abusive users per day from its free tier. ### Pay-As-You-Go Abuse - Customers can consume substantial resources and intentionally avoid paying when billed later. - Radar predicts nonpayment risk as usage accumulates. - Businesses can respond by requiring top-ups, suspending service, or applying other controls before billing. ### Malicious Bot Payments - Radar assigns a bot score to Stripe Checkout payments. - Businesses can distinguish legitimate automated agents from malicious bots. - The score can support controls against: - Inventory hoarding - Promotional abuse - Purchase-limit bypasses - High-velocity automated orders ## Platform and Merchant Risk Management - Platforms receive 0–100 fraud scores for businesses and transactions. - AI-powered explanations, notes, account history, and account-level metrics help risk teams investigate merchants. - New merchant-risk signals include: - **Fraudulent website signal:** Detects suspicious pricing, AI-generated copy, misspelled domains, and other website red flags. - **Fraudulent merchant signal:** Uses business information, bank details, transaction activity, and disputes to identify risky accounts. - **Merchant delinquency risk signal:** Predicts whether a merchant’s negative balance is likely to persist for at least 60 days. - Platforms can use these signals to automate verification, trigger reviews, pause payments or payouts, reject accounts, establish reserves, adjust payout schedules, or request additional identity verification. Stripe’s expanded Radar offering is designed to move fraud prevention earlier in the customer and merchant lifecycle. Businesses and platforms should combine these network-wide signals with their own risk tolerance and workflows to block abuse proactively while minimizing unnecessary friction.

dispute-managementmachine-learningfraud-detectionpayment-processing+4