Stripe Radar vs Riskified

Agent detection

1. ai-native userThe product distinguishes malicious bots from legitimate AI buying agents, so agent-driven purchases aren't blanket-blocked as fraud

   weight 2 · round drawn
   Stripe Radar—none0/10

   The evidence describes Radar's general fraud rules, risk scoring, reviews, and lists, but nothing addresses distinguishing legitimate AI purchasing agents from malicious bots — an axis specific to agentic commerce that is plausible for a fraud-prevention product but unevidenced here. Community feedback even shows false positives blocking legitimate low-risk customers, with no mention of agent-specific allowlisting or detection.

   • [claimed-docs] “Radar Standard: Out-of-the-box fraud protection for all payment methods to detect and prevent transaction fraud, and identify fraudulent acc…”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”

   proof ↗⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

Agent identity

1. developerPass verified agent identity — agentic-payment protocols, signed agent tokens, delegated spending scopes — into the risk decision as a first-class signal

   weight 2 · round drawn
   Stripe Radar—none0/10

   No evidence that Radar accepts verified agent identity, agentic-payment protocol tokens, signed agent tokens, or delegated spending scopes as first-class risk inputs; Radar's documented signals are card, customer, IP, and rule/list based, not agent-identity based.

   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”

   proof ↗⚑ flag

   Riskified—none0/10

   No evidence in the pack mentions agentic-payment protocols, signed agent tokens, or delegated spending scopes as inputs to Riskified's risk decision; the documented signals are customer/order/payment data, HMAC request auth, and Beacon session IDs, none of which represent verified agent identity as a first-class fraud signal.

   ⚑ flag

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round to Stripe Radar
   Stripe Radar✓fullprobed9/10

   Probes confirm a live llms.txt at docs.stripe.com/llms.txt (HTTP 200) and markdown-rendered agent-friendly docs pages (e.g. radar.md), directly enabling an agent to be pointed at agent-oriented docs. Missing for 10: no independent/community confirmation of agents actually consuming these docs successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.stripe.com/llms.txt # Stripe Documentation When installing Stripe packages, always check the npm r…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.stripe.com/radar.md # Radar Use Stripe Radar to protect your business against fraud. ## Get starte…”

   proof ↗⚑ flag

   Riskified✓fullprobed8/10

   A probe confirms Riskified hosts a working llms.txt at developers.riskified.com/llms.txt returning HTTP 200, and docs explicitly note pages can be fetched as markdown by appending .md — exactly the agent-oriented docs pattern. Missing for 10: no independent/third-party corroboration of an agent successfully consuming it, and no broader agent-specific documentation beyond the llms.txt index.

   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.riskified.com/llms.txt # Riskified documentation Documentation > Riskified Documentation Ap…”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to Stripe Radar
   Stripe Radar~partialprobed5/10

   Radar exposes REST API endpoints (rules, value lists, reviews approve/decline) and has dedicated testing docs with test card numbers for automated fraud-rule verification, and Stripe ships an official CLI — all of which support headless/CI use. However, there's no explicit CI/automation guide, and a community report explicitly flags 'lack of easy programmatic control' as a pain point for adjusting Radar decisions. missing for 10: dedicated CI/headless workflow documentation, explicit automation examples, and resolution of the programmatic-control complaint.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “4000000000004954 | Results in a charge with a risk level of `highest`”
   • [probe] “official CLI documented at https://docs.stripe.com/stripe-cli”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”

   proof ↗⚑ flag

   Riskified~partialclaimed4/10

   Riskified is fundamentally an API-first fraud-decision service (decide/submit/advise/chargeback endpoints, SDKs, webhook notifications) that can be called programmatically without a UI, which supports headless/automated integration into checkout pipelines. However, there is no evidence of CI/CD-specific tooling, a CLI, or documented automation-testing workflows — the docs focus on production checkout integration, not build/test automation. Missing for 10: explicit CI/CD examples, a CLI or automation-testing guide, and confirmation of headless operation outside the live transaction flow.

   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “The `/submit` endpoint sends transaction data to Riskified after the gateway authorizes the transaction for fraud analysis... the fraud deci…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”
   • [claimed-docs] “Riskified provides SDKs for several languages to simplify API integration. SDK usage is optional, and all API endpoints can also be accessed…”
   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined... If the endpoint …”

   proof ↗⚑ flag

3. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Stripe Radar
   Stripe Radar✓fullprobed8/10

   Stripe documents an official MCP server (docs.stripe.com/mcp) that lets AI agents connect to Stripe, which as a platform encompasses Radar functionality via its API. Missing for 10: Radar-specific MCP tool examples/independent hands-on corroboration of agent use.

   • [probe] “official MCP server documented at https://docs.stripe.com/mcp”

   proof ↗⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

4. ai-native userUse an official CLI

   weight 2 · round to Stripe Radar
   Stripe Radar✓fullprobed8/10

   Stripe ships an official Stripe CLI (docs.stripe.com/stripe-cli) that covers Radar-related API/webhook workflows, giving AI-native users a scriptable interface. Missing for 10: no direct evidence the CLI has Radar-specific commands or independent hands-on confirmation of its use in agentic workflows.

   • [probe] “official CLI documented at https://docs.stripe.com/stripe-cli”

   proof ↗⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

5. ai-native userDrive the product through a documented public API

   weight 3 · round drawn
   Stripe Radar✓fullprobed8/10

   Stripe Radar exposes documented REST API endpoints (e.g. reviews, early_fraud_warnings, value_lists) and is part of Stripe's broader public API with an official CLI and MCP server, confirming programmatic/agentic access. missing for 10: no discoverable OpenAPI/swagger spec file was found (probe 404s) and no independent hands-on report of an AI agent driving Radar specifically via the API.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.stripe.com/radar.md # Radar Use Stripe Radar to protect your business against fraud. ## Get starte…”
   • [probe] “official MCP server documented at https://docs.stripe.com/mcp”
   • [probe] “official CLI documented at https://docs.stripe.com/stripe-cli”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.stripe.com/openapi.json, https://docs.stripe.com/swagger.json, https://docs.stripe.com/…”

   proof ↗⚑ flag

   Riskified✓fullprobed8/10

   Riskified exposes a comprehensive documented REST API (decide, submit, advise, chargeback, notifications, login) with authentication (HMAC), synchronous/asynchronous flows, and optional SDKs, all directly callable via HTTP — clearly a documented public API an AI-native user could drive. The docs site also exposes an llms.txt for AI-friendly consumption, though no machine-readable OpenAPI/Swagger spec was found (404s on standard paths). Missing for 10: a discoverable OpenAPI/swagger schema and explicit rate-limit/versioning documentation.

   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “The `/submit` endpoint sends transaction data to Riskified after the gateway authorizes the transaction for fraud analysis... the fraud deci…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”
   • [claimed-docs] “Riskified provides SDKs for several languages to simplify API integration. SDK usage is optional, and all API endpoints can also be accessed…”
   • [claimed-docs] “X-RISKIFIED-HMAC-SHA256 | Verification hash for the Request. Generated by performing an SHA256 encryption on the request's POST body and cal…”
   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined... If the endpoint …”
   • [claimed-docs] “Riskified supports different integration types:\n- **Pre-authorization Synchronous**: Review orders before payment authorization\n- **Post-a…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.riskified.com/llms.txt # Riskified documentation Documentation > Riskified Documentation Ap…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://developers.riskified.com/openapi.json, https://developers.riskified.com/swagger.json, https:…”

   proof ↗⚑ flag

6. ai-native userBuild against official SDKs

   weight 2 · round drawn
   Stripe Radar~partialprobed5/10

   The evidence shows official API endpoints (e.g., POST /v1/reviews/:id/approve, early_fraud_warnings API) and an official CLI, implying SDK-compatible API access, but there is no explicit documentation pack entry naming or linking official language SDKs (e.g., stripe-node, stripe-python) for Radar-specific features. missing for 10: explicit SDK documentation/references, code samples showing SDK usage for Radar rules/reviews, independent developer confirmation of SDK coverage.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [probe] “official CLI documented at https://docs.stripe.com/stripe-cli”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.stripe.com/radar.md # Radar Use Stripe Radar to protect your business against fraud. ## Get starte…”

   proof ↗⚑ flag

   Riskified~partialclaimed5/10

   Riskified's docs explicitly state that SDKs are provided for several languages to simplify API integration, confirming official SDK support (riskified-docs-5). However, the evidence lacks specifics such as which languages are covered, links to SDK repositories, or independent/community corroboration of SDK quality. Missing for 10: list of supported languages, links to SDK repos/package registries, hands-on developer feedback on SDK usability.

   • [claimed-docs] “Riskified provides SDKs for several languages to simplify API integration. SDK usage is optional, and all API endpoints can also be accessed…”

   proof ↗⚑ flag

7. ai-native userSubscribe to events via webhooks

   weight 2 · round to Riskified
   Stripe Radar—none0/10

   The evidence pack documents Radar's REST APIs (reviews, early fraud warnings, value lists) but never mentions webhook event subscriptions for these Radar events, so there's no evidence of an AI-native webhook subscription capability despite this being a plausible axis for an API-driven fraud product.

   ⚑ flag

   Riskified~partialclaimed6/10

   Riskified documents a webhook-style notification mechanism: decision outcomes are pushed to a merchant-defined endpoint with retries on non-2xx and HMAC-SHA256 signature verification, which functions as an event webhook for fraud decisions and chargebacks. However, this is a fixed set of built-in notification types (order decision, chargeback) rather than a general-purpose subscribable event system with selectable event topics or a dashboard for managing webhook subscriptions. Missing for 10: a documented event catalog/subscription model letting users choose which event types to receive, and any UI/API for creating or managing multiple webhook subscriptions.

   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined... If the endpoint …”
   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined.”
   • [claimed-docs] “X-RISKIFIED-HMAC-SHA256 | Verification hash for the Request. Generated by performing an SHA256 encryption on the request's POST body and cal…”
   • [claimed-docs] “The `/submit` endpoint sends transaction data to Riskified after the gateway authorizes the transaction for fraud analysis... the fraud deci…”
   • [claimed-docs] “Notifies Riskified that a chargeback has been submitted for a specific order.”

   proof ↗⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round drawn
   Stripe Radar—none0/10

   Radar's docs describe rule-based fraud controls, risk scoring, and dashboard analytics/visualizations (docs-4, docs-12), but there is no evidence of AI-generated natural-language insights or suggestions (e.g., an assistant summarizing fraud trends or recommending rule changes) surfaced inside the product.

   ⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Stripe Radar
   Stripe Radar~partialcommunity6/10

   Radar rules engine runs autonomously in the background on every transaction (auto 3DS requests, auto-pause payouts, custom rules, auto-allow trusted customers, risk settings) per docs-1/2/3/13/14, which is genuine unattended automation. However community feedback notes limited programmatic control over these automations and friction when trying to override or fine-tune them (stripe-radar-comm-2, stripe-radar-comm-3), suggesting the autonomy is somewhat constrained/dashboard-centric rather than fully agent-friendly. missing for 10: evidence of API-driven/programmatic rule creation or agent-triggered automation workflows, and resolution of the 'lack of easy programmatic control' complaint.

   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “Review and automatically pause payouts on accounts that have a high dispute rate”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”

   proof ↗⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round drawn
   Stripe Radar—none0/10

   No evidence of a built-in AI assistant within Stripe Radar to which users can delegate tasks; the product offers rules, lists, reviews, and analytics but no conversational/agentic assistant feature is documented.

   ⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

4. ai-native userOperate the product with natural-language commands

   weight 2 · round drawn
   Stripe Radar—none0/10

   Radar's rule configuration is a structured DSL (attributes/expressions) rather than natural-language commands, and while Stripe has a generic MCP server (stripe-radar-probe-4), there is no evidence it exposes Radar-specific fraud rule management or that Radar can be operated via free-form NL instructions.

   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [probe] “official MCP server documented at https://docs.stripe.com/mcp”

   proof ↗⚑ flag

   Riskified—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round drawn
   Stripe Radar—none0/10

   No evidence of an interactive API reference with runnable examples; the OpenAPI probe returned 404s and docs listed are static markdown pages describing endpoints without runnable/interactive playground features. missing for 10: interactive API explorer/playground, runnable code examples, OpenAPI spec availability.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.stripe.com/openapi.json, https://docs.stripe.com/swagger.json, https://docs.stripe.com/…”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence shows only static API reference docs (endpoints, parameters, notifications) with no mention of an interactive console, runnable examples, or live API explorer; the openapi.json/swagger.json probe returned 404s, indicating no machine-readable spec to power an interactive reference.

   • [probe] “PROBE openapi: all candidate paths 404 (https://developers.riskified.com/openapi.json, https://developers.riskified.com/swagger.json, https:…”
   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “Riskified supports different integration types:\n- **Pre-authorization Synchronous**: Review orders before payment authorization\n- **Post-a…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   Stripe Radar—none0/10

   The evidence pack includes a direct probe for OpenAPI/swagger spec files at common Stripe docs paths, all returning 404, and no other citation shows a downloadable machine-readable spec for Radar's API. Only docs pages and llms.txt-style markdown are confirmed, not an OpenAPI/Swagger file.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.stripe.com/openapi.json, https://docs.stripe.com/swagger.json, https://docs.stripe.com/…”

   proof ↗⚑ flag

   Riskified—none0/10

   A probe explicitly checked common OpenAPI/swagger spec locations and all returned 404, and no evidence pack item references a downloadable machine-readable API spec; only human-readable docs and an llms.txt are present.

   • [probe] “PROBE openapi: all candidate paths 404 (https://developers.riskified.com/openapi.json, https://developers.riskified.com/swagger.json, https:…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.riskified.com/llms.txt # Riskified documentation Documentation > Riskified Documentation Ap…”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Stripe Radar
   Stripe Radar~partialprobed7/10

   Stripe Radar's testing docs explicitly provide test card numbers (e.g., 4000000000004954) that simulate specific risk levels, enabling developers to validate fraud rules and review logic without using real transactions or production data. However, the evidence doesn't detail a full sandbox environment (e.g., test-mode API key isolation, sandbox dashboards) beyond these test cards, and there's no independent/hands-on confirmation of sandbox fidelity for AI-native workflows. Missing for 10: explicit documentation of test-mode/live-mode key separation for Radar, broader sandbox environment description, and independent corroboration of safe non-production testing.

   • [claimed-docs] “4000000000004954 | Results in a charge with a risk level of `highest`”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.stripe.com/radar.md # Radar Use Stripe Radar to protect your business against fraud. ## Get starte…”

   proof ↗⚑ flag

   Riskified—none0/10

   No evidence of a sandbox or test environment separate from production; docs describe live API endpoints, integration flows, and Shopify onboarding but never mention a sandbox/test mode for AI-native or developer testing.

   ⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Stripe Radar—none0/10

   The evidence pack contains no mention of Stripe API versioning scheme, version pinning, or a documented deprecation policy for Radar's API endpoints; only generic docs and community pricing/false-positive discussions are present.

   ⚑ flag

   Riskified—none0/10

   No evidence of API versioning scheme (e.g., v1/v2 paths, version headers) or any documented deprecation policy; OpenAPI spec probes all returned 404. missing for 10: versioned endpoint scheme, deprecation/sunset policy documentation, changelog or migration guides.

   • [probe] “PROBE openapi: all candidate paths 404 (https://developers.riskified.com/openapi.json, https://developers.riskified.com/swagger.json, https:…”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Stripe Radar
   Stripe Radar~partialclaimed5/10

   Radar rules, value lists, and targeted transaction-review lists let a rule or list apply automatically across many transactions/customers at once (stripe-radar-docs-1,2,3,10,11,14), which is a form of bulk automation, but the documented Review API only exposes per-item actions (POST /v1/reviews/:id/approve) with no bulk/batch endpoint or explicit multi-item API call shown. missing for 10: evidence of a bulk API endpoint for approving/declining multiple reviews or disputes at once, and any AI-native tooling for programmatic multi-item operations.

   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “You can create a targeted list of payments to review with criteria that you specify, and review them in the Dashboard.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [claimed-docs] “POST /v1/reviews/:id/approve”

   proof ↗⚑ flag

   Riskified—none0/10

   Riskified's API is order-by-order (decide/submit/advise per transaction), with no evidence of a batch or bulk endpoint for processing many orders/items in a single call, nor any bulk chargeback or data-export operation.

   ⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Stripe Radar
   Stripe Radar~partialcommunity6/10

   Radar's rule engine clearly supports defining conditional rules that trigger automatic actions (3DS challenge, block, review, allow, pause payouts) based on transaction attributes, and value lists let rules be parameterized (docs-1, docs-2, docs-3, docs-10, docs-13, docs-14). However, rule authoring is Dashboard-centric with no documented rules-creation API, and community feedback explicitly flags a 'lack of easy programmatic control' plus restricted access to Allow Rules for newer accounts (comm-2, comm-7), limiting fit for an AI-native/automated workflow. Missing for 10: a documented API/SDK for programmatically creating or updating rules, and evidence of unrestricted automation access for all account types.

   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “Review and automatically pause payouts on accounts that have a high dispute rate”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “Allow Rules, if not implemented properly, could open a vector for fraud. That's why it's not enabled for newer businesses on Stripe—we ask b…”

   proof ↗⚑ flag

   Riskified~partialclaimed3/10

   Riskified automatically triggers fixed actions on fraud-decision events (webhook notifications, Shopify integration auto-voiding/restocking/capturing on approve/decline) but there is no evidence of a user-configurable rules engine where an AI-native user can define custom conditions/actions themselves — the automation is built-in and non-customizable rather than rule-definable. Missing for 10: a rules/condition-authoring API or UI, examples of custom trigger logic, and any AI-native/agentic rule-configuration workflow.

   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined... If the endpoint …”
   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined.”
   • [claimed-docs] “Voids authorization on orders declined by Riskified. Enables automatic restocking of orders declined by Riskified. Captures funds for orders…”
   • [claimed-docs] “Voids authorization on orders declined by Riskified. Enables automatic restocking of orders declined by Riskified. Captures funds for orders…”

   proof ↗⚑ flag

Guarantee

1. finance leadShift fraud liability to the vendor — a chargeback guarantee that reimburses approved-then-disputed orders, with clear coverage terms

   weight 2 · round to Riskified
   Stripe Radar—none0/10

   The evidence pack covers Radar's fraud-scoring, rules, reviews, and dispute-rate monitoring features, but contains no mention of a chargeback guarantee, reimbursement for approved-then-disputed orders, or liability shift terms — that is a distinct Stripe product (Chargeback Protection), not documented here as part of Radar.

   ⚑ flag

   Riskified~partialclaimed5/10

   The evidence pack documents Riskified's API decisioning, chargeback notification, and chargeback gateway integration (automating chargeback reporting/disputing) but never states explicit financial liability-shift terms, reimbursement guarantees, coverage limits, or exclusions that a finance lead would need to evaluate a chargeback guarantee. Chargeback automation is a workflow/process feature, not proof of a monetary guarantee. missing for 10: explicit guarantee/reimbursement terms, coverage scope and exclusions, contractual liability-shift language, any evidence of actual reimbursement payouts.

   • [claimed-docs] “Riskified offers fully automated chargeback gateway integrations for major gateways: Braintree, Stripe, Adyen, PayPal; this process automate…”
   • [claimed-docs] “Notifies Riskified that a chargeback has been submitted for a specific order.”
   • [claimed-docs] “The Chargeback Gateway Integration (CGI) framework enables seamless connectivity with leading payment gateways, including Adyen, Stripe, Bra…”
   • [claimed-docs] “Riskified can send automated decision notifications to a merchant-defined endpoint when an order is approved or declined... If the endpoint …”

   proof ↗⚑ flag

Outcome reporting

1. finance leadSee the numbers that matter — dispute rate, false-positive rate, approval-rate lift, review workload — and export them for the board

   weight 2 · round to Stripe Radar
   Stripe Radar~partialclaimed4/10

   Docs show dispute-rate calculation on the Radar dashboard (stripe-radar-docs-7) and fraud-rate/volume trend visualizations (stripe-radar-docs-4), plus reviewable queues (stripe-radar-docs-11, docs-8) that imply review workload tracking. However there is no evidence of a false-positive-rate metric, approval-rate lift measurement, or any export/reporting feature for board consumption. missing for 10: false-positive rate metric, approval-rate lift metric, CSV/board export capability, independent confirmation of dashboard completeness.

   • [claimed-docs] “We show this calculation on the Radar page in the Dashboard.”
   • [claimed-docs] “Visualize trends in transaction volume and fraud rates over time.”
   • [claimed-docs] “You can create a targeted list of payments to review with criteria that you specify, and review them in the Dashboard.”
   • [claimed-docs] “POST /v1/reviews/:id/approve”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence covers API/integration mechanics (decide, submit, chargeback automation, notifications) but nothing about a finance/reporting dashboard, KPI metrics (dispute rate, false-positive rate, approval-rate lift, review workload), or board-ready export functionality.

   ⚑ flag

Representment

1. ops userChargeback responses are automated — evidence compiled from order, delivery, and session data and submitted to the issuer without manual copy-paste

   weight 3 · round to Riskified
   Stripe Radar—none0/10

   Radar's documented capabilities are fraud scoring, rules, reviews, and dispute-rate monitoring/analytics (docs-2, docs-4, docs-7, docs-9) — none of the evidence describes compiling order/delivery/session evidence and auto-submitting it to card issuers for chargeback responses. This is a distinct dispute-evidence-automation capability that the evidence pack simply does not show Radar performing.

   • [claimed-docs] “We show this calculation on the Radar page in the Dashboard.”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [claimed-docs] “Review and automatically pause payouts on accounts that have a high dispute rate”

   proof ↗⚑ flag

   Riskified~partialclaimed6/10

   Riskified's Chargeback Gateway Integration (CGI) is documented as automating 'end-to-end chargeback handling from chargeback reporting to the disputing process' across major gateways, implying dispute evidence submission without manual copy-paste, and Riskified already ingests order, session (Beacon), and decision data through its APIs. However, the docs don't explicitly describe compiling delivery data or detail the specific evidence package sent to issuers during disputes. Missing for 10: explicit description of delivery-data inclusion in chargeback evidence, detail on the dispute evidence package format submitted to issuers, and independent/hands-on confirmation that submission is fully automated end-to-end.

   • [claimed-docs] “Riskified offers fully automated chargeback gateway integrations for major gateways: Braintree, Stripe, Adyen, PayPal; this process automate…”
   • [claimed-docs] “Notifies Riskified that a chargeback has been submitted for a specific order.”
   • [claimed-docs] “The Chargeback Gateway Integration (CGI) framework enables seamless connectivity with leading payment gateways, including Adyen, Stripe, Bra…”
   • [claimed-docs] “Riskfied's Beacon is a JavaScript snippet that merchants embed on their website. It collects fundamental data required to decide on the legi…”
   • [claimed-docs] “The `/submit` endpoint sends transaction data to Riskified after the gateway authorizes the transaction for fraud analysis... the fraud deci…”

   proof ↗⚑ flag

Agent operations

1. ai-native userAn agent can read my fraud posture and manage rules and lists programmatically — propose a velocity rule, update a blocklist — with human approval gates

   weight 3 · round to Stripe Radar
   Stripe Radar!disputedcontradicted4/10

   Stripe exposes APIs for value lists (blocklists/allowlists) and review approval (a human-approval gate: POST /v1/reviews/:id/approve), plus a documented MCP server, which together could let an agent read fraud data and manage lists with approval steps. However, rule creation/velocity-rule authoring is Dashboard-centric with no documented rules-create API, and community evidence directly contradicts the 'agent manages rules programmatically' premise: users report 'the lack of easy programmatic control is an issue for us' and that Allow Rules are disabled by default for newer accounts requiring a manual support request to enable. Missing for 10: a documented API/MCP tool to create or propose new velocity/fraud rules, and independent confirmation that programmatic rule/list management works smoothly without support intervention.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [probe] “official MCP server documented at https://docs.stripe.com/mcp”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “Allow Rules, if not implemented properly, could open a vector for fraud. That's why it's not enabled for newer businesses on Stripe—we ask b…”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence covers order review/decision APIs, notifications, chargeback automation, and Beacon data collection, but there is no mention of programmatic management of fraud rules, velocity rules, or blocklists, nor any agent-facing approval workflow for such changes.

   ⚑ flag

Agent triage

1. ai-native userAn agent can work the review queue — pull flagged cases with their context, summarize the evidence, and recommend a decision for a human to confirm

   weight 2 · round to Stripe Radar
   Stripe Radar~partialprobed6/10

   Radar exposes a Reviews API (list/retrieve/approve reviews), risk-insights showing related-payment networks, and early-fraud-warning data that an agent could pull as 'flagged case context,' and Stripe documents an official MCP server that could expose these APIs to an agent. However there's no first-party feature for automated evidence summarization or a recommend-then-human-confirm workflow — that logic would have to be built by the integrator, and community reports note UI/override friction around review decisions. Missing for 10: a documented agent/summarization workflow for reviews, evidence the MCP server actually exposes the reviews/early-fraud-warning endpoints, and independent confirmation of an agent successfully working the queue end-to-end.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “You can create a targeted list of payments to review with criteria that you specify, and review them in the Dashboard.”
   • [claimed-docs] “You can also view the network of related payments, which includes any other payments made to your business using the same customer ID, IP ad…”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [probe] “official MCP server documented at https://docs.stripe.com/mcp”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”

   proof ↗⚑ flag

   Riskified—none0/10

   Riskified's evidence describes automated decisioning APIs (/decide, /submit, /advise), notifications, and merchant integrations, but nothing describes an agent-accessible review queue with case context retrieval, evidence summarization, or a human-confirm workflow. No API for pulling flagged cases with context or generating human-reviewable recommendations is documented.

   ⚑ flag

Builtin ai

1. risk analystThe product ships its own AI assistant — natural-language queries over my fraud data, drafted rules, investigation summaries — built into the console

   weight 2 · round drawn
   Stripe Radar—none0/10

   No evidence of a built-in AI assistant in the Radar console for natural-language queries, drafted rules, or investigation summaries; documentation covers rules engine, reviews, analytics, and lists but nothing about an AI/NLP assistant feature.

   ⚑ flag

   Riskified—none0/10

   Evidence covers API endpoints, webhooks, chargeback automation, Beacon SDK, and integration processes, but nothing describes a built-in AI assistant, natural-language query interface, rule drafting, or investigation summaries in the console.

   ⚑ flag

Abuse coverage

1. risk analystProtection extends beyond checkout — account takeover, fake account creation, promo and policy abuse are scored and managed in the same system

   weight 2 · round drawn
   Stripe Radar~partialclaimed5/10

   Radar Pro docs explicitly extend beyond checkout fraud to 'multi-account, free trial, and pay-as-you-go abuse,' covering fake-account and promo/policy abuse in the same platform, but there is no evidence of dedicated account-takeover detection or scoring — the docs focus on payment/charge risk, reviews, and disputes rather than login/session anomaly detection typical of ATO protection. Missing for 10: explicit account-takeover detection/scoring capability, unified dashboard evidence showing ATO alongside promo-abuse cases, and independent confirmation these abuse types are actually managed in one system rather than just marketed together.

   • [claimed-docs] “Radar Pro: Advanced protection against emerging fraud threats and customer abuse. Detect multi-account, free trial, and pay-as-you-go abuse.”
   • [claimed-docs] “Radar Standard: Out-of-the-box fraud protection for all payment methods to detect and prevent transaction fraud, and identify fraudulent acc…”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”

   proof ↗⚑ flag

   Riskified~partialclaimed5/10

   Riskified's Login API explicitly scores account-takeover attempts (allow/notify/challenge) alongside its core checkout fraud decisioning (decide/submit/advise) and automated chargeback handling, showing the same system spans multiple fraud surfaces beyond checkout. However, there is no documentation of fake-account-creation detection or promo/coupon abuse scoring as distinct managed surfaces. Missing for 10: explicit fake-account-creation protection, promo/policy abuse scoring, and independent confirmation these are unified in one decisioning system.

   • [claimed-docs] “API is triggered only for customer-initiated authentication requests, e.g., user tries to login to their account... `decision` field will be…”
   • [claimed-docs] “`decision` field will be set as one of the following values: `allow`, `notify`, or `challenge`.”
   • [claimed-docs] “Riskified offers fully automated chargeback gateway integrations for major gateways: Braintree, Stripe, Adyen, PayPal; this process automate…”
   • [claimed-docs] “Notifies Riskified that a chargeback has been submitted for a specific order.”
   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”

   proof ↗⚑ flag

Integrations

1. ops userThere are maintained integrations for my commerce stack — Shopify, Salesforce Commerce, BigCommerce, and the major PSPs — not just a raw API

   weight 2 · round to Riskified
   Stripe Radar—none0/10

   Evidence covers Radar's rules, reviews, risk settings, and API/CLI/MCP tooling, but nothing mentions maintained integrations or plugins for commerce platforms like Shopify, Salesforce Commerce, or BigCommerce, or PSP-specific integrations beyond Stripe's own API. Missing for 10: any documentation of Shopify/BigCommerce/Salesforce Commerce app integrations, partner PSP integrations, or an integrations marketplace listing.

   • [claimed-docs] “Radar Standard: Out-of-the-box fraud protection for all payment methods to detect and prevent transaction fraud, and identify fraudulent acc…”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.stripe.com/radar.md # Radar Use Stripe Radar to protect your business against fraud. ## Get starte…”

   proof ↗⚑ flag

   Riskified~partialclaimed7/10

   Riskified documents maintained platform integrations beyond raw API: a dedicated Shopify app with automated order actions (docs-11,12,21,22), a Salesforce Commerce Cloud/Agentforce Commerce fraud cartridge (docs-10), and automated Chargeback Gateway Integration with major PSPs (Braintree, Stripe, Adyen, PayPal) (docs-4,16,17). However, there is no mention of a BigCommerce integration, and the PSP integrations are scoped to chargeback data only rather than full checkout/decisioning flows. Missing for 10: explicit BigCommerce integration, evidence of maintained PSP integrations beyond chargebacks (e.g., payment/checkout-time connectors), independent confirmation of integration quality/maintenance cadence.

   • [claimed-docs] “Riskified offers fully automated chargeback gateway integrations for major gateways: Braintree, Stripe, Adyen, PayPal; this process automate…”
   • [claimed-docs] “Agentforce Commerce (formerly Salesforce Commerce Cloud / SFCC) fraud protection cartridge provides a structured integration process tailore…”
   • [claimed-docs] “Voids authorization on orders declined by Riskified. Enables automatic restocking of orders declined by Riskified. Captures funds for orders…”
   • [claimed-docs] “If we have questions about shop flows or encounter data issues, we will reach out to you for clarification... Once data validation passes, y…”
   • [claimed-docs] “The Chargeback Gateway Integration (CGI) framework enables seamless connectivity with leading payment gateways, including Adyen, Stripe, Bra…”
   • [claimed-docs] “Once you have installed the Riskified app in your Shopify store, the integration process can begin. During this time, most of the “heavy lif…”
   • [claimed-docs] “Voids authorization on orders declined by Riskified. Enables automatic restocking of orders declined by Riskified. Captures funds for orders…”

   proof ↗⚑ flag

Psp coverage

1. developerUse the product across whatever payment stack I run — multiple PSPs, gateways, and platforms — rather than being locked to one processor's rails

   weight 3 · round to Riskified
   Stripe Radar—none0/10

   All evidence describes Radar as a feature built directly into Stripe's own payments processing (rules, reviews, risk settings, session tokenization, testing tied to Stripe test cards) with no mention of usable integration with other PSPs, gateways, or platforms. Fraud-detection tools in general could plausibly support multi-processor use, but nothing in the pack shows Radar operating outside Stripe's own rails — it is described purely as a Stripe-native capability.

   • [claimed-docs] “Radar Standard: Out-of-the-box fraud protection for all payment methods to detect and prevent transaction fraud, and identify fraudulent acc…”
   • [claimed-docs] “By using Radar Sessions, you can capture critical fraud information without tokenizing on Stripe.”
   • [claimed-docs] “4000000000004954 | Results in a charge with a risk level of `highest`”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.stripe.com/radar.md # Radar Use Stripe Radar to protect your business against fraud. ## Get starte…”

   proof ↗⚑ flag

   Riskified~partialclaimed6/10

   Riskified is gateway/platform-agnostic via its own API (/decide, /submit, /advise), with automated chargeback integrations naming specific gateways (Braintree, Stripe, Adyen, PayPal) and platform cartridges for Shopify and SFCC, indicating it can plug into multiple PSPs/platforms rather than lock a merchant into one processor. However, the evidence doesn't explicitly enumerate broad PSP/gateway compatibility beyond the four named chargeback gateways, nor discuss developer-facing flexibility across arbitrary payment stacks explicitly. missing for 10: a comprehensive list of supported PSPs/gateways beyond chargeback integrations, explicit statements about processor-agnostic architecture, and independent confirmation of multi-PSP interoperability.

   • [claimed-docs] “Riskified offers fully automated chargeback gateway integrations for major gateways: Braintree, Stripe, Adyen, PayPal; this process automate…”
   • [claimed-docs] “The Chargeback Gateway Integration (CGI) framework enables seamless connectivity with leading payment gateways, including Adyen, Stripe, Bra…”
   • [claimed-docs] “Agentforce Commerce (formerly Salesforce Commerce Cloud / SFCC) fraud protection cartridge provides a structured integration process tailore…”
   • [claimed-docs] “Voids authorization on orders declined by Riskified. Enables automatic restocking of orders declined by Riskified. Captures funds for orders…”
   • [claimed-docs] “Riskified supports different integration types:\n- **Pre-authorization Synchronous**: Review orders before payment authorization\n- **Post-a…”

   proof ↗⚑ flag

Explainability

1. risk analystEvery score comes with its top risk factors — why this transaction looks risky — not just an opaque number

   weight 3 · round to Stripe Radar
   Stripe Radar~partialclaimed4/10

   Radar's Reviews/Risk Insights docs show a 'network of related payments' (same customer ID, IP, card) and analytics trends, suggesting some contextual signals behind a score, but the evidence never describes an explicit list of 'top risk factors' or feature-level explanation attached to each transaction's score. Missing for 10: explicit per-transaction factor breakdown/SHAP-style explanation, quantified factor weighting, independent confirmation that risk-insights actually enumerates specific risk drivers rather than just related-payment context.

   • [claimed-docs] “You can also view the network of related payments, which includes any other payments made to your business using the same customer ID, IP ad…”
   • [claimed-docs] “Visualize trends in transaction volume and fraud rates over time.”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence describes decision outcomes (approve/decline/allow/notify/challenge) and notification mechanisms, but nothing indicates that decisions are accompanied by explanatory risk factors or reason codes for analysts. No mention of feature-level explanations, contributing signals, or reason breakdowns anywhere in the docs.

   ⚑ flag

Model evaluation

1. finance leadMeasure the model itself — precision and recall on my traffic, shadow-mode trials of new models or rules before they take over decisions

   weight 2 · round drawn
   Stripe Radar—none0/10

   Docs mention fraud-rate analytics and dispute measurement (fraud insights, risk settings) but nowhere describe precision/recall metrics on the merchant's own traffic or a shadow-mode mechanism to trial new models/rules before they affect decisions. Rules can be created and reviewed, but there's no evidence of a non-blocking 'test' or 'shadow' deployment mode for models.

   • [claimed-docs] “Visualize trends in transaction volume and fraud rates over time.”
   • [claimed-docs] “We show this calculation on the Radar page in the Dashboard.”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”

   proof ↗⚑ flag

   Riskified—none0/10

   The evidence pack only covers API endpoints, integrations, notifications, and chargeback automation — nothing about model performance metrics (precision/recall), model versioning, or shadow-mode testing of new models/rules before go-live is documented.

   ⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Stripe Radar
   Stripe Radar!disputedcontradicted4/10

   Stripe exposes API endpoints for some Radar objects (reviews approve/reject, early fraud warnings, value lists) but the core capability of authoring/editing Radar Rules and risk settings is documented only via Dashboard-oriented docs (stripe-radar-docs-3, docs-13) with no corresponding rules-API endpoint evidenced. Community feedback explicitly calls out this gap ('The lack of easy programmatic control is an issue for us', stripe-radar-comm-2) and notes Allow Rules require manual support intervention rather than self-service API access (stripe-radar-comm-7), directly contradicting full API/UI parity. missing for 10: documented API endpoints for creating/editing Radar rules, programmatic risk-settings control, independent confirmation that all dashboard actions have API equivalents.

   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “Allow Rules, if not implemented properly, could open a vector for fraud. That's why it's not enabled for newer businesses on Stripe—we ask b…”

   proof ↗⚑ flag

   Riskified—none0/10

   The evidence pack documents Riskified's API endpoints (decide, submit, advise, chargeback, notifications) and SDKs, but never discusses the merchant dashboard/UI or compares what can be done there versus via API, so there's no evidence of feature parity between UI and API.

   ⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Stripe Radar—none0/10

   No evidence in the pack addresses data residency, regional storage options, or data location controls for Stripe Radar; the documentation covers fraud rules, reviews, and risk settings but not where data is stored.

   ⚑ flag

   Riskified—none0/10

   No evidence in the pack mentions data residency, region selection, or storage location options for merchant/customer data; documentation covers API endpoints, integrations, and notifications only.

   ⚑ flag

2. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Stripe Radar—none0/10

   The evidence pack contains no mention of data retention policies, deletion controls, or privacy/data lifecycle management features for Radar; documentation covers fraud rules, reviews, testing, and pricing but nothing about controlling how long data is kept or deleting it.

   ⚑ flag

   Riskified—none0/10

   No evidence pack items address data retention policies, deletion mechanisms, or user/customer data control tools; the docs focus entirely on fraud-decision APIs and integrations with no privacy-posture or data lifecycle controls mentioned.

   ⚑ flag

Residency

1. ops userControl where fraud data lives and how long it's kept — regional residency options and retention controls that survive a privacy review

   weight 2 · round drawn
   Stripe Radar—none0/10

   No evidence pack item addresses data residency options, regional storage location controls, or configurable retention periods for fraud/Radar data; the docs cover rules, reviews, lists, and testing but nothing about compliance/residency controls.

   ⚑ flag

   Riskified—none0/10

   No evidence in the pack addresses data residency options, regional data storage, or retention/deletion controls; documentation covers API flows, integrations, and authentication only.

   ⚑ flag

Sca

1. developerEuropean traffic is routed intelligently through SCA — 3DS triggered when required or risky, exemptions requested when safe — to protect both compliance and conversion

   weight 2 · round to Riskified
   Stripe Radar~partialclaimed4/10

   Radar rules docs show it can trigger 3DS for specific conditions (e.g., new customers) via custom rules, but there is no evidence of intelligent SCA-wide routing that automatically requests exemptions (TRA, low-value, etc.) to preserve conversion — the exemption side of the story is unaddressed and this Radar-specific capability differs from Stripe's core SCA/Payment Intents engine. missing for 10: evidence of automated exemption requests (TRA/low-value/trusted-beneficiary), evidence of end-to-end SCA compliance logic beyond manual rule authoring, and independent confirmation of conversion impact.

   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”

   proof ↗⚑ flag

   Riskified✓fullclaimed7/10

   Riskified's /advise endpoint explicitly returns CVV/3DS recommendations and adds TRA/SCA exemption recommendations for regulated markets, directly matching the story of intelligently triggering 3DS or requesting exemptions per order. This is first-party documentation with no independent corroboration of real-world accuracy or European-specific behavior. Missing for 10: independent/hands-on validation of SCA exemption success rates, and explicit detail on how 'European traffic' specifically is distinguished/routed versus other regions.

   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... In addition, CVV/3DS recommendations can be prov…”

   proof ↗⚑ flag

Case review

1. risk analystFlagged transactions land in a review queue that shows the full context — customer history, signals, similar cases — so I can decide quickly and consistently

   weight 3 · round to Stripe Radar
   Stripe Radar~partialcommunity7/10

   Stripe's docs show a genuine review queue workflow: targeted transaction reviews (docs-11), an approve/reject API (docs-8), and a 'risk insights' view showing related payments across customer ID, IP, or card number for spotting similar cases (docs-12). However, evidence doesn't explicitly confirm a unified single screen combining full customer history + fraud signals + similar-case surfacing in one glance, and community feedback notes UI friction when analysts try to act on flagged transactions (comm-3, unclear override controls). missing for 10: explicit documentation of a consolidated 'customer history' panel within the review UI, and independent/hands-on confirmation that the queue enables fast, consistent decisions without friction.

   • [claimed-docs] “You can create a targeted list of payments to review with criteria that you specify, and review them in the Dashboard.”
   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “You can also view the network of related payments, which includes any other payments made to your business using the same customer ID, IP ad…”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence covers Riskified's API endpoints (/decide, /submit, /advise), notifications, chargeback automation, and Beacon data collection, but nothing describes a human review-queue UI showing analyst context, customer history, or similar cases for manual decisioning — Riskified's documented flow is automated/API-driven decisioning, not analyst-facing case review tooling.

   ⚑ flag

Feedback loop

1. risk analystMy review decisions and confirmed fraud outcomes feed back into the model and rules, so the system learns from every case we work

   weight 2 · round to Stripe Radar
   Stripe Radar~partialclaimed4/10

   Radar lets analysts approve/decline reviews (docs-8) and maintain allow/block value lists that then drive future rule evaluation (docs-10, docs-14), which is a manual feedback mechanism, and early fraud warnings feed dispute-rate risk signals (docs-9, docs-7). However there is no documented evidence that confirmed fraud outcomes or review decisions automatically retrain Radar's underlying ML model — the docs describe rules/lists as merchant-configured, not an automated learning loop tied to case outcomes. Missing for 10: explicit documentation of the ML model being retrained from analyst decisions/fraud confirmations, and independent/hands-on confirmation that outcomes measurably change future scoring.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [claimed-docs] “We show this calculation on the Radar page in the Dashboard.”

   proof ↗⚑ flag

   Riskified—none0/10

   The evidence describes automated decisioning APIs (/decide, /submit, /advise), chargeback notification integrations, and onboarding model calibration by Riskified's own analytics team, but nothing describes an analyst-facing review queue where human review decisions or confirmed fraud outcomes are captured and fed back into the model/rules on an ongoing basis. Missing for 10: evidence of an analyst review UI, a mechanism for analysts to submit manual review/fraud confirmations, and documentation that such decisions retrain or update the fraud model/rules.

   • [claimed-docs] “If we have questions about shop flows or encounter data issues, we will reach out to you for clarification... Once data validation passes, y…”
   • [claimed-docs] “Notifies Riskified that a chargeback has been submitted for a specific order.”
   • [claimed-docs] “Riskified offers fully automated chargeback gateway integrations for major gateways: Braintree, Stripe, Adyen, PayPal; this process automate…”

   proof ↗⚑ flag

Team workflows

1. ops userReview work is a team workflow — assignment, escalation, SLAs, and a decision audit trail that shows who approved what and why

   weight 2 · round to Stripe Radar
   Stripe Radar~partialclaimed3/10

   Radar's reviews API supports approve/decline actions on flagged payments and lets teams build custom review queues, which implies some decision logging, but there is no documented support for assignment to specific reviewers, escalation paths, or SLA tracking, and no explicit 'who approved what and why' audit trail beyond the approve/reject call itself. missing for 10: reviewer assignment, escalation workflow, SLA tracking, structured decision-rationale audit trail.

   • [claimed-docs] “POST /v1/reviews/:id/approve”
   • [claimed-docs] “You can create a targeted list of payments to review with criteria that you specify, and review them in the Dashboard.”
   • [claimed-docs] “You can also view the network of related payments, which includes any other payments made to your business using the same customer ID, IP ad…”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence covers API decisioning, notifications, chargeback automation, and integrations, but nothing describes a human ops review workflow with case assignment, escalation, SLAs, or an audit trail of who approved what and why.

   ⚑ flag

Custom signals

1. developerFeed the model my own signals — device fingerprints, behavioral data, custom metadata — so scoring reflects my business, not just network defaults

   weight 2 · round to Stripe Radar
   Stripe Radar~partialcommunity5/10

   Radar lets developers write custom rules against 'supported attributes' and value lists (docs-3, docs-10, docs-14), and Radar Session captures device/browser signals for fraud evaluation without full tokenization (docs-15) — this shows some capacity to incorporate custom signals. However, there's no evidence that arbitrary custom metadata or behavioral data actually feeds into or retrains Radar's core ML risk score itself (rather than just triggering rule-based overrides), and community commentary notes a 'lack of easy programmatic control' over scoring (comm-2). missing for 10: explicit documentation that custom metadata/behavioral inputs alter the underlying risk score model, first-party guidance on feeding proprietary signals into scoring, and independent confirmation this works as intended.

   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [claimed-docs] “By using Radar Sessions, you can capture critical fraud information without tokenizing on Stripe.”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”

   proof ↗⚑ flag

   Riskified—none0/10

   Riskified's API endpoints (/decide, /submit, /advise) and Beacon.js collect a fixed, vendor-defined set of order/behavioral data required for fraud analysis, and model calibration is explicitly handled by Riskified's own analytics team (docs-12), not the developer. There is no evidence of an API or schema allowing developers to inject custom device fingerprints, arbitrary behavioral signals, or custom metadata fields into the scoring model to tailor it to their own business logic.

   • [claimed-docs] “Riskfied's Beacon is a JavaScript snippet that merchants embed on their website. It collects fundamental data required to decide on the legi…”
   • [claimed-docs] “If we have questions about shop flows or encounter data issues, we will reach out to you for clarification... Once data validation passes, y…”
   • [claimed-docs] “The `/submit` endpoint sends transaction data to Riskified after the gateway authorizes the transaction for fraud analysis... the fraud deci…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”

   proof ↗⚑ flag

Network effects

1. founderScoring benefits from a cross-merchant network — a card or identity seen across thousands of other businesses informs the risk decision on mine

   weight 2 · round to Stripe Radar
   Stripe Radar~partialclaimed5/10

   Docs show Radar surfaces a 'network of related payments' across customer ID, IP, or card number (docs-12) and ingests card-issuer signals via Early Fraud Warnings (docs-9), implying some cross-account risk signal, and docs-6 describes 'out-of-the-box' pre-trained fraud detection. However, none of the evidence explicitly states the model is trained on or scores against Stripe's full cross-merchant network of thousands of businesses. Missing for 10: explicit documentation of the network-wide ML training/scoring claim, and independent confirmation that cross-merchant signals (not just same-business history) drive individual risk scores.

   • [claimed-docs] “You can also view the network of related payments, which includes any other payments made to your business using the same customer ID, IP ad…”
   • [claimed-docs] “An early fraud warning indicates that the card issuer has notified us that a charge may be fraudulent.”
   • [claimed-docs] “Radar Standard: Out-of-the-box fraud protection for all payment methods to detect and prevent transaction fraud, and identify fraudulent acc…”

   proof ↗⚑ flag

   Riskified—none0/10

   The evidence pack only documents Riskified's API endpoints, SDKs, and integration flows (decide, submit, advise, chargeback, notifications, Beacon, login) — none of it describes a cross-merchant data network, shared fraud signals across Riskified's merchant base, or how identity/card history from other businesses informs a given merchant's score. Without any documentation of the network-effect mechanism, this axis has no supporting evidence.

   ⚑ flag

Score actions

1. ops userMap score ranges to actions — allow, review, block, step-up 3DS — and tune thresholds to my own risk appetite instead of a fixed cutoff

   weight 2 · round to Stripe Radar
   Stripe Radar~partialcommunity6/10

   Stripe Radar's docs clearly support mapping risk levels to actions (block, review, request 3DS, allow via value lists) and tuning via risk-settings/rules with custom attributes, directly matching the story. However, community evidence shows real friction: allow rules are gated behind manual support enablement for newer accounts (comm-7), an ops user hit a 'block' override button that silently did nothing requiring a support ticket (comm-3), and another reports 'lack of easy programmatic control' over false positives (comm-2), indicating the threshold-tuning experience isn't as self-service as docs imply. Missing for 10: independent verification that threshold-to-action mapping is fully self-serve without support intervention, and resolution of the UI override bug.

   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”
   • [community] “Allow Rules, if not implemented properly, could open a vector for fraud. That's why it's not enabled for newer businesses on Stripe—we ask b…”

   proof ↗⚑ flag

   Riskified—none0/10

   Riskified's docs describe its endpoints (/decide, /submit, /advise, login) returning pre-computed decisions like allow/notify/challenge or approve/decline, but there is no evidence that ops users can view underlying risk scores or configure/tune score-to-action thresholds themselves — decisioning appears to be Riskified's managed model rather than a merchant-tunable scorecard.

   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”
   • [claimed-docs] “API is triggered only for customer-initiated authentication requests, e.g., user tries to login to their account... `decision` field will be…”
   • [claimed-docs] “`decision` field will be set as one of the following values: `allow`, `notify`, or `challenge`.”

   proof ↗⚑ flag

Scoring api

1. developerGet a machine-learning risk score for a transaction in real time — synchronously, before authorization completes — through a documented API

   weight 3 · round to Riskified
   Stripe Radar~partialclaimed6/10

   Docs confirm Radar attaches a risk level/score to each charge (e.g. test card 4000000000004954 'Results in a charge with a risk level of highest') and that rules/reviews act on this score, implying the score is computed as part of normal payment processing and exposed on the charge object via the API. However, the pack never explicitly documents the exact API field (e.g. charge.outcome.risk_score) or explicitly states the score is available synchronously before authorization completes, and no independent/hands-on confirmation of real-time synchronous scoring is present. missing for 10: explicit API reference/schema for risk_score/risk_level field, explicit documentation stating the score is returned before/at authorization time, independent corroboration of real-time synchronous behavior.

   • [claimed-docs] “4000000000004954 | Results in a charge with a risk level of `highest`”
   • [claimed-docs] “Radar Standard: Out-of-the-box fraud protection for all payment methods to detect and prevent transaction fraud, and identify fraudulent acc…”
   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “POST /v1/reviews/:id/approve”

   proof ↗⚑ flag

   Riskified✓fullprobed7/10

   Riskified's documented `/decide` endpoint explicitly returns a synchronous fraud decision before payment authorization completes, matching the real-time pre-auth workflow described in the story, and Riskified's own docs classify this as 'Pre-authorization Synchronous' integration. However, the docs describe a categorical decision (approve/decline) rather than an explicit numeric ML risk score, and no OpenAPI/swagger spec exists (probe returned 404s) to confirm formal API schema. missing for 10: explicit numeric risk-score field documentation, machine-readable OpenAPI spec, independent/hands-on corroboration of latency/real-time behavior.

   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “Riskified supports different integration types:\n- **Pre-authorization Synchronous**: Review orders before payment authorization\n- **Post-a…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://developers.riskified.com/openapi.json, https://developers.riskified.com/swagger.json, https:…”

   proof ↗⚑ flag

Backtesting

1. risk analystBacktest a rule against my historical traffic before deploying it, seeing exactly what it would have blocked, flagged, and cost

   weight 2 · round drawn
   Stripe Radar—none0/10

   The evidence pack covers rule creation, value lists, risk settings, reviews, and analytics dashboards, but no documentation or community evidence describes a backtesting/simulation feature that shows what a new rule would have blocked/flagged/cost against historical traffic before deployment.

   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Visualize trends in transaction volume and fraud rates over time.”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence covers Riskified's fraud-decision APIs, notifications, chargeback integrations, and merchant integration flows, but nothing describes a rule backtesting capability against historical traffic showing what would have been blocked/flagged/cost before deployment.

   ⚑ flag

Lists

1. ops userMaintain allow and block lists — emails, cards, devices, IPs — and velocity limits, managed through the dashboard and programmatically

   weight 2 · round to Stripe Radar
   Stripe Radar!disputedcontradicted5/10

   Stripe documents value lists for allow/block lists (customers, cards, emails, IPs) manageable via API and dashboard (stripe-radar-docs-10, docs-14) plus rules for velocity/custom attributes (docs-3, docs-1), giving vendor-side coverage of the story. However hands-on community reports contradict smooth programmatic control: one user cites 'lack of easy programmatic control' causing false-positive grief, and another found the dashboard's block-override button non-functional, requiring a support email to whitelist transactions; Stripe itself confirms Allow Rules are gated behind manual support enablement for newer accounts, not self-serve. Missing for 10: clear documentation of dedicated device/IP allow-and-block management (only cards/customers/emails are explicit), and no evidence the programmatic control gap or override bug has been resolved.

   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”
   • [community] “Allow Rules, if not implemented properly, could open a vector for fraud. That's why it's not enabled for newer businesses on Stripe—we ask b…”

   proof ↗⚑ flag

   Riskified—none0/10

   Evidence covers order decisioning APIs, notifications, chargebacks, and integrations, but nothing describes allow/block lists for emails, cards, devices, or IPs, nor velocity limit configuration via dashboard or API.

   ⚑ flag

Rule authoring

1. risk analystAuthor custom rules that combine model scores, velocity counters, list matches, and transaction attributes into allow, block, or review decisions

   weight 3 · round to Stripe Radar
   Stripe Radar~partialcommunity7/10

   Stripe docs show a rich rules engine — custom rules using risk score, transaction attributes, value lists (for list matches), and allow/block/review outcomes (docs-1,3,10,13,14) — which covers most of the story. However, community evidence shows secondary caveats: Allow Rules are disabled by default for newer businesses and require manual support enablement (comm-7), and users report inability to override block decisions via UI plus false positives (comm-2,3), indicating real friction in the rule-authoring/decisioning workflow. Missing for 10: explicit documentation/example of velocity-counter attributes in rule syntax, and independent confirmation that all rule types (allow/block/review) work reliably without support intervention.

   • [claimed-docs] “Request 3D Secure (3DS) for all payments that support it and are made by a new customer”
   • [claimed-docs] “you can also set up rules that are unique to your business using the supported attributes”
   • [claimed-docs] “Value lists allow you to group values together which can then be referenced in rules.”
   • [claimed-docs] “Risk settings let you balance authorization and fraud on your account by using risk controls.”
   • [claimed-docs] “Customer IDs for trusted customers: Use this list to automatically allow payments by these customers.”
   • [community] “Allow Rules, if not implemented properly, could open a vector for fraud. That's why it's not enabled for newer businesses on Stripe—we ask b…”
   • [community] “We got a couple cases of false positives ourselves, and the Stripe UI wasn't very clear that we couldn't override the 'block' (the button wa…”
   • [community] “We are seeing an opposite side: customers using Stripe that had very low fraud rates previously are now getting more false positives causing…”

   proof ↗⚑ flag

   Riskified—none0/10

   Riskified's docs describe API endpoints (decide/submit/advise), notifications, and integrations, but there is no evidence of a user-facing custom rules authoring interface where a risk analyst can combine model scores, velocity counters, list matches, and transaction attributes into allow/block/review logic. The decisions described (allow/notify/challenge, approve/decline) are produced by Riskified's own models, not by analyst-authored rules. Missing for 10: any rule-builder UI, rule syntax/DSL, ability to combine velocity counters/list matches/model scores into custom logic, or analyst-facing rule management docs.

   • [claimed-docs] “The `/decide` endpoint is used to request that Riskified review an order for fraud after the customer has input their payment data and compl…”
   • [claimed-docs] “For each order reviewed by advise we provide a synchronous decision if it is clear fraud... CVV/3DS recommendations can be provided... TRA/S…”
   • [claimed-docs] “API is triggered only for customer-initiated authentication requests, e.g., user tries to login to their account... `decision` field will be…”
   • [claimed-docs] “`decision` field will be set as one of the following values: `allow`, `notify`, or `challenge`.”

   proof ↗⚑ flag