Amazon Fraud Detector
Amazon Fraud Detector is a fully managed service that uses machine learning to identify potentially fraudulent activities and accurately distinguish between legitimate and high-risk transactions. It uses your data and the same technology that Amazon uses to protect its own business from fraud.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Amazon Fraud Detector the way a machine reads it — 56 machine-readable artifacts across 7 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon Fraud Detector scores 69.3/100 (strong), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Amazon Fraud Detector
Each block below is one kind of artifact we hold for Amazon Fraud Detector. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 7
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Amazon Fraud Detector Detectors API
Fraud detector configurations
Amazon Fraud Detector Event Types API
Event schema definitions
Amazon Fraud Detector Labels API
Fraud and legitimate transaction labels
Amazon Fraud Detector Models API
ML model training and versioning
Amazon Fraud Detector Predictions API
Real-time fraud prediction
Amazon Fraud Detector Rules API
Business logic rules for fraud decisions
Amazon Fraud Detector Tags API
Resource metadata labels
Scroll within the panel for all 7 ·
Postman Collections 1
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Amazon Fraud Detector API
POSTMANOpen Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Amazon Fraud Detector API
OPEN COLLECTIONArazzo Workflows 8
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Amazon Fraud Detector Author Rule
Create a DETECTORPL rule for a detector and read the detector's rules back to confirm it.
ARAZZOAmazon Fraud Detector Bootstrap Event Type
Create fraud and legit labels, define an event type that uses them, then confirm the event type exists.
ARAZZOAmazon Fraud Detector Decommission Detector
Inspect a detector's rules and then delete the detector, branching when rules still block deletion.
ARAZZOAmazon Fraud Detector Detector Pipeline
Define an event type, create a detector and a rule, then score a sample event against the detector.
ARAZZOAmazon Fraud Detector Inventory Models and Detectors
List models for an event type, then list detectors and tag a chosen detector with its model count.
ARAZZOAmazon Fraud Detector Provision Model and Detector
Define an event type, create an ML model and a detector on top of it, then confirm the detector exists.
ARAZZOAmazon Fraud Detector Score Event and Tag
Score an event against a detector and branch on the returned model score to tag the detector accordingly.
ARAZZOAmazon Fraud Detector Tag and Audit Resource
Assign tags to a Fraud Detector resource and read its tags back to confirm they were applied.
ARAZZOScroll within the panel for all 8 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Amazon Fraud Detector Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Features 7
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
No ML Expertise Required
Automatically trains and deploys ML models using your historical transaction data without requiring ML expertise.
Real-Time Fraud Scoring
Returns fraud scores within milliseconds for integration into transaction approval flows.
Pre-Built Models
Online Fraud Insights (OFI), Transaction Fraud Insights (TFI), and Account Takeover Insights (ATI) pre-trained model types.
Rule Engine
DETECTORPL rule language allows writing conditional logic using model scores and event variables.
Model Explainability
Variable importance scores explain which factors most influenced a fraud prediction.
Cold Start Protection
Uses Amazon fraud experience to provide immediate predictions even with limited historical data.
Event Ingestion
Ingest historical labeled events to continuously improve model accuracy over time.
Scroll within the panel for all 7 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Spectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon Fraud Detector API Rules
SPECTRALAmazon Fraud Detector API Rules
SPECTRALJSON Schema 5
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
JSON Structure 5
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Amazon Fraud Detector Detector Structure
JSON STRUCTUREAmazon Fraud Detector Event Type Structure
JSON STRUCTUREAmazon Fraud Detector Model Structure
JSON STRUCTUREAmazon Fraud Detector Rule Structure
JSON STRUCTUREAmazon Fraud Detector Tag Structure
JSON STRUCTUREExamples 5
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 4
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Payment Fraud Detection
Score credit card and payment transactions in real-time to block fraudulent purchases.
Account Takeover Prevention
Detect unauthorized login attempts and account compromise using behavioral signals.
New Account Fraud
Identify fraudulent new account registrations at signup to prevent synthetic identity fraud.
Promotion Abuse Detection
Flag users abusing discount codes, referral bonuses, and promotional offers.
Chargeback Prevention
Reduce chargeback rates by blocking high-risk transactions before they complete.
Insurance Claims Fraud
Score insurance claims for fraudulent patterns in real-time during claim submission.
Resources
Every other property we hold for Amazon Fraud Detector — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 11
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 11 ·
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 4
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/amazon-fraud-detector · machine-readable index on apis.io