Stripe Radar vs Signifyd

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

   Signifyd—none0/10

   The evidence pack covers Signifyd's fraud-decision API, webhooks, device profiling, chargebacks, and refund/return tooling, but nothing addresses distinguishing malicious bots from legitimate AI shopping/purchasing agents or any agentic-commerce-specific fraud logic. Missing for 10: any mention of AI agent identification, agent-vs-bot classification, or policy to avoid blocking legitimate AI buying agents.

   ⚑ 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

   Signifyd—none0/10

   No evidence in the pack mentions agentic-payment protocols, signed agent tokens, or delegated spending scopes being ingested as risk signals; the API docs cover standard order/checkout/sale/chargeback events and device profiling only.

   ⚑ 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

   Signifyd—none0/10

   The llms.txt probe returned a 404, showing no dedicated agent-oriented manifest exists, and while a markdown-rendered docs page was found at one URL, there's no evidence of a systematic agent-oriented docs structure or llms.txt file across the site. missing for 10: a working llms.txt or equivalent agent-discoverable docs index, and any indication docs are structured/announced for AI agent consumption.

   • [probe] “PROBE llms.txt: HTTP 404 at https://developer.signifyd.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 200 at https://developer.signifyd.com/main/reference/introduction.md --- updatedAt: 2026-01-06T12:05:16.000Z --- # Fund…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   Signifyd~partialclaimed5/10

   Signifyd's core product is a REST API (with SDKs and webhooks) intended for backend/server-side integration rather than a UI-dependent tool, which inherently supports headless/programmatic use in automated pipelines. However, there is no explicit documentation of CI-specific tooling, a CLI, or automated-testing guidance — missing for 10: CI-specific setup docs, CLI/automation tooling, service-account/API-key guidance for pipeline use, and independent confirmation of headless CI usage.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”
   • [claimed-docs] “SDKs provide mobile application developers with self-contained libraries for implementing Signifyd. Each SDK include instructions, examples,…”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round drawn
   Stripe Radar—none0/10

   Evidence only shows Stripe publishes an official MCP *server* (docs.stripe.com/mcp) exposing its own tools to external agents, not that Radar itself can act as an MCP client consuming other servers' tools. No documentation or community evidence shows Radar being configured with external MCP servers to extend its own functionality.

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

   proof ↗⚑ flag

   Signifyd—none0/10

   No evidence of an official MCP server or any MCP integration for Signifyd; the evidence only covers its REST API, webhooks, and SDKs. As a fraud-protection SaaS, this axis is fair to expect but there is no documentation or claim of MCP server support.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [probe] “PROBE llms.txt: HTTP 404 at https://developer.signifyd.com/llms.txt”

   proof ↗⚑ flag

4. 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

   Signifyd—none0/10

   Signifyd is a fraud-protection SaaS with a REST API and webhooks, but there is no evidence of an official MCP server for agent connectivity; the llms.txt probe even 404s. missing for 10: any official MCP server, documentation, or first-party endpoint enabling agent connectivity.

   • [probe] “PROBE llms.txt: HTTP 404 at https://developer.signifyd.com/llms.txt”

   proof ↗⚑ flag

5. 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

   Signifyd—none0/10

   No evidence of any official CLI tool; Signifyd only documents REST API endpoints, SDKs for mobile apps, and webhooks, with no mention of a command-line interface for AI-native workflows.

   ⚑ flag

6. 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

   Signifyd✓fullprobed8/10

   Signifyd provides a comprehensive, well-documented REST API covering authentication, sale/checkout events, decisions, chargebacks, webhooks, and SDKs, confirmed by both docs and a live probe returning 200 with actual API content. Missing for 10: no llms.txt or explicit AI-agent-oriented documentation, and no independent third-party corroboration of API usability.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”
   • [probe] “PROBE docs-md: HTTP 200 at https://developer.signifyd.com/main/reference/introduction.md --- updatedAt: 2026-01-06T12:05:16.000Z --- # Fund…”

   proof ↗⚑ flag

7. 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

   Signifyd~partialclaimed5/10

   Signifyd documents official mobile SDKs with instructions, examples, and project files, plus a REST API reference for direct integration, but the evidence lacks concrete language-specific SDKs (Python, Node, Java, etc.), GitHub repos, or versioning/release info typical of AI-native SDK-driven workflows. missing for 10: language/server-side SDK documentation, GitHub repo links, code samples showing SDK usage beyond mobile.

   • [claimed-docs] “SDKs provide mobile application developers with self-contained libraries for implementing Signifyd. Each SDK include instructions, examples,…”
   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”

   proof ↗⚑ flag

8. ai-native userSubscribe to events via webhooks

   weight 2 · round to Signifyd
   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

   Signifyd✓fullclaimed7/10

   Signifyd has documented webhook creation (createteamwebhook) and configuration docs describing real-time notifications for guarantee decisions, directly supporting event subscription via webhooks. Missing for 10: independent/hands-on corroboration of webhook reliability, full event-type catalog, and payload schema documentation.

   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”
   • [claimed-docs] “Signifyd can send a webhook to notify your online store every time a guarantee decision is made on a submitted order. This provides you with…”

   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

   Signifyd—none0/10

   The evidence pack shows API endpoints, webhooks, SDKs, and marketing blurbs like 'Return Insights' offering 'actionable intelligence,' but nothing describes AI-generated insights, natural-language explanations, or suggestions surfaced to users inside the product. No dashboard, chat, or generative-AI feature is documented.

   • [claimed-docs] “Return Insights Actionable intelligence to reduce returns and protect revenue”
   • [claimed-docs] “Instant Refunds Deliver instant, risk-free refunds that drive loyalty and revenue”

   proof ↗⚑ 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

   Signifyd~partialclaimed4/10

   Signifyd's core product (fraud decisioning, chargeback handling, webhook notifications) runs autonomously in the background once integrated, and webhooks/API endpoints let a merchant configure automated event flows (e.g. createteamwebhook, getdecision, createchargeback) that fire without manual intervention. However, there is no evidence of an AI-native automation builder, scheduler, or agent-configurable workflow system — the 'automation' here is fixed product behavior wired via API/webhooks rather than a user-defined autonomous automation platform. Missing for 10: evidence of a user-configurable automation/rules engine, scheduling or trigger-condition builder, and any AI-specific automation tooling.

   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”
   • [claimed-docs] “Signifyd can send a webhook to notify your online store every time a guarantee decision is made on a submitted order. This provides you with…”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”

   proof ↗⚑ 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

   Signifyd—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

   Signifyd—none0/10

   Signifyd's evidence pack only shows a REST API and webhook integration for fraud/chargeback management; there is no mention of natural-language command interfaces, chat-based control, or NL-driven operation of the product. missing for 10: any NL command interface, chatbot/assistant control surface, or documented natural-language API layer.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”

   proof ↗⚑ 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

   Signifyd—none0/10

   Evidence shows Signifyd has an extensive REST API reference (endpoints for Sale, Checkout, Decision, Chargeback, Webhooks, etc.) but nothing in the pack indicates an interactive console, runnable code samples, or 'try it now' functionality typical of an AI-native API reference. Missing for 10: any mention of interactive/runnable request builder, live sandbox execution, or SDK-embedded runnable snippets.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”

   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

   Signifyd—none0/10

   The evidence pack shows Signifyd has REST API reference docs (built on a docs platform) but no mention of a downloadable OpenAPI/Swagger spec file or machine-readable schema anywhere in the pack, and a direct probe for llms.txt returned 404 with no OpenAPI equivalent found.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [probe] “PROBE llms.txt: HTTP 404 at https://developer.signifyd.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 200 at https://developer.signifyd.com/main/reference/introduction.md --- updatedAt: 2026-01-06T12:05:16.000Z --- # Fund…”

   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

   Signifyd—none0/10

   No evidence pack item mentions a sandbox, test environment, or test mode for Signifyd's API; all evidence describes production-oriented endpoints (Sale, Checkout, chargeback, webhooks) with no mention of a separate testing environment. Missing for 10: any sandbox/test API docs, test credentials or environment flags, or independent confirmation of a non-production testing mode.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Integrating Signifyd on your online stores enables orders placed on your stores to be sent to Signifyd for Guaranteed Fraud Protection.”

   proof ↗⚑ 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

   Signifyd—none0/10

   Evidence shows a REST API with numerous documented endpoints, but nothing indicates API versioning scheme or a documented deprecation policy anywhere in the docs pack. missing for 10: explicit API version numbering, changelog, deprecation notice/policy, sunset timelines.

   ⚑ 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

   Signifyd—none0/10

   The evidence shows only per-order/per-event API endpoints (Sale, Checkout, Chargeback, etc.) with no batch/bulk endpoints or documentation of processing multiple items in a single call.

   ⚑ 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

   Signifyd—none0/10

   Signifyd's API/webhook docs support event ingestion and notifications, but there's no evidence of a user-facing rules engine where AI-native users can define custom trigger-condition-action automations. missing for 10: rule-definition interface, conditional trigger/action configuration, evidence of automation builder.

   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”
   • [claimed-docs] “Signifyd can send a webhook to notify your online store every time a guarantee decision is made on a submitted order. This provides you with…”

   proof ↗⚑ flag

3. ai-native userVersion, review, and roll back my automations

   weight 1 · round drawn
   Stripe Radar—none0/10

   No evidence of version history, rule change review/audit trail, or rollback capability for Radar rules/automations; rules are managed via dashboard/API but no versioning or rollback mechanism is documented. Missing for 10: rule version history, diff/review UI, rollback/restore functionality.

   ⚑ flag

   Signifyd—none0/10

   No evidence of any versioning, review, or rollback mechanism for automations/rules/workflows in Signifyd; the docs only cover API endpoints for fraud decisioning, webhooks, and chargebacks. Missing for 10: version history UI, audit trail for rule/automation changes, and rollback capability.

   ⚑ 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 Signifyd
   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

   Signifyd✓fullclaimed8/10

   Signifyd's docs describe the core chargeback-guarantee mechanics: 'Guaranteed Fraud Protection' on integrated orders, a Chargeback Event API to record disputes, Representment Outcome tracking, and explicit guarantee-cancellation rules tied to returns/refunds, plus a dedicated 'Chargeback Recovery' offering. This directly matches the liability-shift/reimbursement story with documented workflow and terms touchpoints (cancellation conditions, representment outcomes). Missing for 10: explicit contractual coverage terms/limits (e.g., reimbursement caps, eligibility exclusions) and independent/customer confirmation that claims are actually paid out as promised.

   • [claimed-docs] “Integrating Signifyd on your online stores enables orders placed on your stores to be sent to Signifyd for Guaranteed Fraud Protection.”
   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “To set the outcome of a Chargeback's Representment in the Signifyd system, you need to record a Representment Outcome Event.”
   • [claimed-docs] “Once a return, refund, or appeasement has been completed, merchants can cancel the Signifyd Guarantee associated with the order.”
   • [claimed-docs] “Chargeback Recovery Take charge of your chargebacks”

   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

   Signifyd—none0/10

   Evidence covers API endpoints for sales, checkouts, chargebacks, webhooks, and device profiling, but nothing mentions a finance/board-facing dashboard, KPI reporting (dispute rate, false-positive rate, approval-rate lift, review workload), or export functionality for reporting purposes. Missing for 10: any documentation of analytics/reporting dashboards, defined KPI metrics, or export/board-reporting features.

   • [claimed-docs] “Chargeback Recovery Take charge of your chargebacks”
   • [claimed-docs] “Return Insights Actionable intelligence to reduce returns and protect revenue”
   • [claimed-docs] “Instant Refunds Deliver instant, risk-free refunds that drive loyalty and revenue”

   proof ↗⚑ 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 Signifyd
   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

   Signifyd~partialclaimed5/10

   Signifyd's API includes explicit Chargeback and Representment Outcome endpoints (createchargeback, representmentoutcome) and a dedicated 'Chargeback Recovery' product line, indicating dispute handling automation exists, but the evidence never details the automated compilation of order/delivery/session evidence or direct submission to issuers without manual intervention. missing for 10: documentation of automatic evidence bundling from order/delivery/session data, proof of direct issuer submission, and any hands-on/independent confirmation of end-to-end automation.

   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “To set the outcome of a Chargeback's Representment in the Signifyd system, you need to record a Representment Outcome Event.”
   • [claimed-docs] “Chargeback Recovery Take charge of your chargebacks”

   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

   Signifyd—none0/10

   The evidence shows a REST API for order/event submission, decisions, chargebacks, and webhooks, but nothing about rules/lists management (e.g., velocity rules, blocklists) or an agentic workflow with human approval gates. No documentation of a rules API or agent-oriented approval mechanism is present.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”

   proof ↗⚑ 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

   Signifyd—none0/10

   Evidence shows only standard REST endpoints for sale/checkout events, decision retrieval, chargebacks, and webhooks — nothing about a queue of flagged cases with case context, an agent summarizing evidence, or generating a recommendation for human confirmation. No case-management or review-queue API surface is documented at all.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”

   proof ↗⚑ 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

   Signifyd—none0/10

   Evidence covers only REST API endpoints, webhooks, SDKs, and documentation for fraud data/order management — nothing about a built-in AI assistant, natural-language query interface, rule drafting, or investigation summaries in the console. missing for 10: any mention of an AI/NLP assistant feature, natural-language query capability, AI-drafted rules, or AI-generated investigation summaries.

   ⚑ 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 to Stripe Radar
   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

   Signifyd—none0/10

   Evidence covers checkout/payment fraud, chargebacks, returns, and refund protection, but nothing addresses account takeover, fake account creation, or promo/policy abuse scoring within Signifyd's system. Missing for 10: any documentation of account takeover protection, fake account detection, or promo/policy abuse scoring features.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “Once a return, refund, or appeasement has been completed, merchants can cancel the Signifyd Guarantee associated with the order.”

   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 drawn
   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

   Signifyd—none0/10

   Evidence only covers Signifyd's raw REST API, webhooks, SDKs, and device-profiling script — there is no mention of maintained platform-specific integrations (Shopify app, Salesforce Commerce Cloud cartridge, BigCommerce app, or PSP-specific connectors). No evidence names or links any prebuilt/maintained integration for a specific commerce platform or PSP.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “Integrating Signifyd on your online stores enables orders placed on your stores to be sent to Signifyd for Guaranteed Fraud Protection.”
   • [claimed-docs] “SDKs provide mobile application developers with self-contained libraries for implementing Signifyd. Each SDK include instructions, examples,…”

   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 Signifyd
   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

   Signifyd~partialclaimed6/10

   Signifyd's API is generic (Checkout/Sale events, webhooks) and explicitly designed to sit outside the payment flow — the Checkout event is called 'before calling your Payment Gateway' — implying gateway/PSP-agnostic architecture rather than lock-in to one processor's rails. However, the evidence pack never explicitly lists supported PSPs, gateways, or platforms, nor confirms interoperability across multiple stacks simultaneously. missing for 10: explicit documentation naming supported PSPs/gateways/platforms, and independent confirmation of multi-PSP usage in production.

   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Integrating Signifyd on your online stores enables orders placed on your stores to be sent to Signifyd for Guaranteed Fraud Protection.”
   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”

   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

   Signifyd—none0/10

   The evidence pack covers API endpoints for submitting orders, retrieving decisions, webhooks, chargebacks, and device profiling, but none of it describes the decision/score payload including risk factor breakdowns or explanations behind a fraud score. The getdecision endpoint doc only says it 'retrieves the latest fraud decision' with no mention of contributing risk factors or explainability.

   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”

   proof ↗⚑ 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

   Signifyd—none0/10

   Evidence covers API integration, webhooks, chargeback/return workflows, and device profiling, but there is no mention of model performance metrics (precision/recall), shadow-mode testing, or A/B evaluation of decisioning models exposed to customers. Missing for 10: precision/recall reporting on customer traffic, shadow-mode/challenger model trials, any model-transparency or evaluation tooling.

   ⚑ flag

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

   weight 2 · round to Signifyd
   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

   Signifyd~partialclaimed5/10

   Signifyd's API covers core fraud-protection workflows (sale/checkout events, decisions, chargebacks, webhooks, device profiling) that mirror much of what merchants would configure in the UI, suggesting broad API/UI parity for core fraud operations. However, there is no evidence confirming that dashboard-only features like Return Insights, Instant Refunds management, or Chargeback Recovery workflows are fully API-accessible, nor any explicit statement of full UI/API parity. missing for 10: explicit parity statement, API coverage for analytics/reporting features (Return Insights, Instant Refunds), evidence of account/team management via API.

   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “To set the outcome of a Chargeback's Representment in the Signifyd system, you need to record a Representment Outcome Event.”
   • [claimed-docs] “Create a new webhook. This will add to the list of any existing webhooks.”
   • [claimed-docs] “Once a return, refund, or appeasement has been completed, merchants can cancel the Signifyd Guarantee associated with the order.”
   • [claimed-docs] “Return Insights Actionable intelligence to reduce returns and protect revenue”
   • [claimed-docs] “Instant Refunds Deliver instant, risk-free refunds that drive loyalty and revenue”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round drawn
   Stripe Radar—none0/10

   No evidence of any data export feature or open-format data portability for Radar; docs cover rules, reviews, analytics, and APIs but nothing about exporting all account/fraud data to leave the platform. missing for 10: bulk/full data export tooling, open-format (CSV/JSON) export docs, any account-closure/data-portability guarantee.

   ⚑ flag

   Signifyd—none0/10

   Evidence shows a REST API for submitting order/checkout/chargeback data into Signifyd and retrieving decisions, but nothing indicates a full data-export capability or open-format bulk export/portability feature for users to take all their data and leave. No mention of GDPR-style export, data portability tooling, or account deletion/export flows. missing for 10: bulk/full data export feature, open-format (CSV/JSON) export documentation, account closure/data portability guarantee.

   • [claimed-docs] “This document will show you how to use Signifyd's REST API to authenticate, make requests, and retrieve data.”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”

   proof ↗⚑ 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

   Signifyd—none0/10

   No evidence in the pack mentions data residency, regional storage options, or any configurability of where data is stored; only API/webhook/fraud-decision docs are present. missing for 10: any mention of data residency/region selection, hosting locations, or compliance controls for data storage.

   ⚑ 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

   Signifyd—none0/10

   Evidence only covers API endpoints, webhooks, and sensitive-data rejection at the API layer; there is no documentation of data retention policies, user-initiated deletion controls, or data lifecycle management. Missing for 10: retention policy documentation, deletion/export APIs or controls, data lifecycle configuration options.

   ⚑ 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

   Signifyd—none0/10

   Evidence covers API endpoints, webhooks, SDKs, and a sensitive-data protection layer, but nothing addresses regional data residency options or configurable retention/deletion policies that would survive a privacy review. Missing for 10: documented data residency regions, retention period controls, data deletion/export tooling, and any compliance certification (e.g., GDPR/SOC2) mapping to residency.

   • [claimed-docs] “Signifyd has implemented various protocols at the API layer that will reject requests that are identified to contain unwanted sensitive data…”

   proof ↗⚑ 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 Stripe Radar
   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

   Signifyd—none0/10

   The evidence pack covers Signifyd's fraud/guarantee API (Sale, Checkout, chargebacks, webhooks, device profiling) but contains no mention of SCA, 3DS, PSD2 exemptions, or any European payment-authentication routing logic. Nothing indicates the product decides when to trigger 3DS versus request an exemption. missing for 10: any documentation of SCA/3DS handling, exemption request logic, or PSD2-specific routing features.

   ⚑ 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

   Signifyd—none0/10

   Evidence pack covers Signifyd's API endpoints (Sale, Checkout, chargeback, webhooks, device profiling) but contains no mention of a review queue UI, case management dashboard, or consolidated view showing customer history, signals, and similar cases for analysts. No evidence supports the analyst-facing review workflow described in the story.

   ⚑ 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

   Signifyd~partialclaimed3/10

   Signifyd's API lets merchants record Chargeback and Representment Outcome events, which feed confirmed fraud outcomes into the system, but the evidence pack never states that analyst review-queue decisions or these outcomes are used to retrain the model or update rules. Missing for 10: explicit documentation of a review-queue/analyst decision feedback loop, evidence of model retraining or rule updates from confirmed outcomes, and any hands-on/independent confirmation that this loop improves detection.

   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “To set the outcome of a Chargeback's Representment in the Signifyd system, you need to record a Representment Outcome Event.”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”

   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

   Signifyd—none0/10

   Evidence covers API endpoints for fraud decisions, chargebacks, webhooks, and device profiling, but nothing about a human review-queue workflow with assignment, escalation, SLAs, or a decision audit trail showing who approved what and why. Missing for 10: assignment/queue management docs, escalation workflows, SLA tracking, human-approval audit trail features.

   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “To create a chargeback against an Order in the Signifyd system, you need to record a Chargeback Event.”
   • [claimed-docs] “Signifyd can send a webhook to notify your online store every time a guarantee decision is made on a submitted order. This provides you with…”

   proof ↗⚑ 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

   Signifyd~partialclaimed4/10

   Signifyd's Sale/Checkout API endpoints let developers submit order and event data, and device-profiling script captures fingerprints, but these are Signifyd's own structured schemas and proprietary device fingerprinting rather than a documented mechanism for injecting arbitrary custom behavioral signals or metadata into scoring. Missing for 10: explicit API fields for custom/behavioral metadata, documentation on how custom signals influence the risk score, and independent confirmation that scoring reflects merchant-specific inputs beyond standard order fields.

   • [claimed-docs] “The Sale endpoint in Signifyd's API is used to record a Sale Event, which represents the completion of a purchase flow and the associated pa…”
   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Place the following script just before the closing `</head>` tag on your checkout page. The script loads asynchronously and does not affect …”
   • [claimed-docs] “Signifyd has implemented various protocols at the API layer that will reject requests that are identified to contain unwanted sensitive data…”

   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

   Signifyd—none0/10

   The evidence pack covers Signifyd's API endpoints, webhooks, device profiling, chargebacks, and guarantees, but contains no mention of a cross-merchant network effect, shared consortium data, or how data from other merchants informs a given merchant's risk score. missing for 10: any documentation of network-wide identity/card matching, consortium data sharing, or cross-merchant signal aggregation.

   ⚑ 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

   Signifyd—none0/10

   Evidence covers Signifyd's API endpoints for orders, decisions, chargebacks, and webhooks, but nothing describes configurable score-to-action mapping (allow/review/block/step-up 3DS) or threshold tuning for ops users; the getdecision endpoint only retrieves a decision, not a configurable rule engine.

   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “Signifyd can send a webhook to notify your online store every time a guarantee decision is made on a submitted order. This provides you with…”

   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 Stripe Radar
   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

   Signifyd~partialclaimed5/10

   Signifyd's Checkout endpoint is documented to be called before the payment gateway in a Pre-Auth flow, aligning with the 'before authorization' requirement, and there is a documented GetDecision endpoint to retrieve the fraud decision. However, the evidence does not show the Checkout call synchronously returning an ML risk score in its response; decision retrieval appears to depend on a separate GetDecision call or webhook, suggesting an asynchronous decisioning pattern rather than an inline synchronous score. Missing for 10: explicit documentation that the Checkout API response includes a real-time ML score synchronously, and confirmation that no polling/webhook wait is required before authorization completes.

   • [claimed-docs] “The Checkout endpoint in Signifyd's API is used to record a Checkout Event in a Pre-Auth flow. This call should be made _before_ calling you…”
   • [claimed-docs] “Retrieve the latest fraud decision for an order in the Signifyd system.”
   • [claimed-docs] “Signifyd can send a webhook to notify your online store every time a guarantee decision is made on a submitted order. This provides you with…”

   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

   Signifyd—none0/10

   No evidence pack items mention rule backtesting, simulation against historical traffic, or previewing block/flag/cost outcomes before deployment; the docs cover API integration, webhooks, chargebacks, and device profiling only.

   ⚑ 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

   Signifyd—none0/10

   The evidence pack covers Signifyd's REST API for sale/checkout events, decisions, chargebacks, webhooks, and device profiling, but contains no mention of allow/block lists, velocity limits, or list management endpoints/dashboard features for emails, cards, devices, or IPs.

   ⚑ 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

   Signifyd—none0/10

   The evidence pack covers API endpoints for sale/checkout events, webhooks, decisions, chargebacks, and device profiling, but contains no mention of a rules engine or capability for analysts to author custom rules combining model scores, velocity counters, list matches, and transaction attributes into allow/block/review decisions.

   ⚑ flag