stripeAug 11, 2026

Mapping the AI economy (opens in new tab)

Stripe’s data shows that AI companies are expanding internationally at remarkable speed, but the largest markets are not always the most promising growth opportunities. India, Mexico, Poland, the UAE, and South Korea stand out when considering AI spending relative to overall online spending and growth rates. The central conclusion is that global availability starts expansion, while localization—especially payment methods and currencies—drives lasting revenue. ## AI Spending Reveals Emerging Markets - The largest AI-spending markets on Stripe are generally high-GDP countries with strong online commerce and global connectivity. - Looking at AI spending as a share of total Stripe spending highlights less obvious opportunities: - India, Mexico, Poland, and the UAE have unusually high AI spending relative to their overall payment volume. - Brazil, Japan, and South Korea combine substantial absolute AI spending with a high relative share. - These signals can help companies prioritize expansion beyond conventional large-market strategies. ## AI Market Growth Is Broad but Uneven - Among 35 markets with more than $20 million in AI spending by 2024, median year-over-year growth was nearly 100%. - Large markets sometimes grew below the median but still delivered significant absolute expansion: - The United States grew 91%. - Australia grew 61%. - Canada, Germany, and the UK sustained strong growth despite already having considerable AI spending. - South Korea was a particularly attractive market, combining: - 134% growth - A large existing market - AI spending disproportionate to total Stripe spending - Mexico was the standout, with AI spending growth of 264%. Its proximity to the US and lower competitive saturation may make it an appealing expansion target. ## Global Availability Must Be Followed by Localization - Companies can launch globally very quickly. Manus accepted payments in more than 200 countries and territories within a month of its 2025 breakout, reaching a $90 million run rate four months later. - However, maintaining international growth requires adapting products and payment experiences to local markets. - The fastest-growing AI companies use roughly twice as many local payment methods as the broader AI-company cohort. - Stripe data suggests local payment methods can increase: - Conversion by an average of 7.4% - Revenue by an average of 12% - Gamma increased revenue in India by 22% after adding UPI, and more than half of its total revenue now comes from outside the US. - Local-currency pricing also improves results for subscription businesses: - Adaptive Pricing increased initial conversion by 4.7% on average. - It increased lifetime subscription value by 5.4%. - Runway achieved up to 17.7% higher lifetime value per subscription. AI companies should use market size, relative AI demand, and growth momentum to select expansion targets, then invest in local payment methods, currency support, translations, and regional marketing. Global launch creates reach, but deep localization is what turns that reach into durable international revenue.

payment-processingaistripeadaptive-pricing+3

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

payment-processingaistripefraud-prevention+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.

payment-processingmachine-learningfraud-detectionbot-detection+4

stripeMar 24, 2026

How Stripe Radar helps prevent free trial abuse (opens in new tab)

Free trial abuse is accelerating, particularly among AI companies whose trials provide access to costly compute resources. Stripe detected 6.2 times more abusive trials between November 2025 and February 2026, with self-serve AI startups facing especially high exposure. Stripe argues that AI-powered fraud detection can identify abuse at signup and prevent substantial downstream losses. ## The rise of free trial abuse - Fraudsters increasingly cycle through free trials or use invalid payment methods without converting to paid plans. - AI companies are especially vulnerable because free trials can grant access to expensive compute and APIs. - AI startups with self-serve signup and direct API access experience 10 times more attempted abuse than enterprise AI companies. - Similar patterns affect SaaS companies, marketplaces, and other businesses offering free trials. ## Stripe Radar’s abuse-prevention controls - Stripe Radar now offers a one-click control to detect behavior violating common trial terms, including repeated signups and missed cancellations. - The system predicts abusive behavior with 90% accuracy. - A new analytics page displays blocked high-risk payments and, for unenrolled businesses, shows transactions that would have been blocked. - The model analyzes payment instruments, devices, payment history, card BIN data, virtual card indicators, email domains, session timing, and other risk signals across Stripe’s network. ## Results for AI companies - Cursor and other AI businesses use Radar to block suspicious users before they consume costly compute. - Within two months, Stripe blocked over 550,000 high-risk free trials across four high-growth AI companies. - Stripe estimates this prevented $4.4 million in downstream compute-related losses. Stripe recommends its free trial abuse control for businesses across industries. Companies interested in early access can contact Stripe directly.

payment-processingaistripesaas+4

stripeMar 10, 2026

Analyzing first-party fraud trends: Account, free trial, and refund abuse (opens in new tab)

First-party fraud is rising as legitimate customers exploit account, trial, and refund policies rather than using stolen credentials. Stripe’s analysis identifies account abuse, free-trial abuse, and refund fraud as rapidly growing problems, with AI companies particularly exposed because free access consumes costly compute resources. Stripe is expanding Radar with tools to detect these behaviors across the customer lifecycle. ## Account Abuse at Sign-Up - Users create multiple accounts to repeat free trials, reuse promotional offers, or evade fraud detection. - A single payment method may be linked to dozens or hundreds of emails, IP addresses, and names. - About 20% of consumers admit to using different contact details to access promotions repeatedly; the figure rises to 29% among Gen Z and 27% among millennials. - AI companies are especially vulnerable because repeated free-tier access consumes compute resources. Stripe found suspected multiaccount abuse in 7.4% of AI-company sign-ups. - Stripe is introducing Radar capabilities to assess sign-ups and login events, helping businesses distinguish genuine prospects from repeat abusers. ## Free-Trial Abuse and Virtual Cards - Customers may cycle through multiple trials to extend free access beyond the stated terms. - AI startups with self-serve registration and direct API access experience 10 times more attempted abuse than enterprise AI offerings. - Blocking virtual cards is no longer an effective solution because many legitimate customers use them for privacy and security. - Stripe’s new solution predicts common trial-term abuse with 90% accuracy. - Radar also provides analytics showing blocked high-risk payments and, for businesses without the control enabled, payments that would have been blocked. ## Refund Abuse After Purchase - Customers may falsely claim that products were defective or never delivered while keeping the merchandise. - Stripe estimates global refund-abuse losses at roughly $100 billion annually. - “Wardrobing”—wearing items briefly before returning them—was admitted by 27% of shoppers who returned an online purchase, rising to 49% among Gen Z shoppers. - Social-media shopping hauls can create costs through return shipping, processing, markdowns, and unsellable inventory. - Organized abusers may use more than 100 email variations and multiple cards to bypass refund limits and “no questions asked” policies. - Because purchases often use valid credentials, the abuse may only become visible after the refund is issued. - Stripe is developing tools to identify refund abuse and is seeking preview participants. ## Stripe’s Broader Fraud-Prevention Strategy - Stripe plans to use its network data, existing AI infrastructure, and Radar to detect repeat abusers, fake-account networks, and emerging first-party fraud tactics. - The broader objective is to monitor and reduce abuse throughout registration, trial access, payment, and post-purchase refund processes. Businesses should treat first-party fraud as a lifecycle-wide risk rather than relying only on transaction-time fraud checks. More targeted, AI-based detection can reduce abuse without unnecessarily rejecting legitimate users, especially those using virtual cards.

payment-processingaistripefraud-prevention+3