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Stripe Radar for Platforms

Stripe Radar for Platforms is the platform-tier variant of Stripe Radar, targeting marketplaces and software platforms whose risk surface is merchants signing up on the platform rather than (or in addition to) end-buyer fraud. Disclosed in the 2026-05-27 Sessions roundup as part of the Radar expansion.

Surface

  • 0-to-100 fraud score for every business and every transaction.
  • AI-powered insights explaining why an account is flagged.
  • Account history + note-taking so platform fraud teams can share context.
  • Account-level metrics for disputes, declines, refunds, payments.

Merchant-risk signals

Three new signals introduced in the 2026-05-27 disclosure:

  • Fraudulent website — "analyzes a business's website the way a human fraud analyst would, looking for red flags like luxury items sold at unrealistically low prices, AI-generated copy, misspelled brand URLs, or other indicators that suggest the site is fraudulent." Use cases: automate verifications during onboarding, flag accounts for manual review, feed an internal risk-scoring pipeline.
  • Fraudulent merchant — "identifies whether a new or existing account poses a fraud risk, based on analyzing patterns across the Stripe network, including bank account information, business details, transaction activity, and disputes." Platform actions: raise a review, pause payouts, pause payments, reject the account, set reserves, request identity verification.
  • Merchant delinquency risk — "predicts whether a business is at risk of accruing a negative balance; specifically, it predicts whether that balance is likely to remain negative for 60 days or more." 60-day horizon makes this a forward-looking financial-loss prediction distinct from current-state fraud risk. Platform actions: proactively adjust payout schedules, require reserves on high-risk accounts, flag for review before losses accumulate.

Architectural framing

The post's framing on why this matters: "Fraudulent actors are using generative AI to create fake identities, documents, and websites convincing enough to bypass many platforms' verification systems. Platforms face a trade-off: request additional information during onboarding and increase friction, or keep the onboarding flow lightweight and take on potentially significant risk."

Radar for Platforms is positioned as the AI-vs-AI defence: detection of generative-AI-augmented fraud needs detection signals trained on the network's accumulated fraud patterns rather than per-platform manual review.

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