Fraugster

Fraugster is a Berlin-based artificial intelligence company focused on payment fraud prevention and transaction risk scoring for online merchants, payment service providers, and acquirers. Its machine-learning platform was designed to analyze payments in real time to detect and block fraudulent transactions while reducing false declines and chargebacks. The company was surfaced through venture portfolio data (backed by Seedcamp) and added to the API Evangelist network. During this enrichment pass the company's public developer surface was not reachable to automated clients (the primary domain returns HTTP 403), so no API specifications, SDKs, or documentation could be harvested; only live infrastructure/domain-security signals were probed.

Fraugster is profiled on the APIs.io network. Tagged areas include Company, Fraud Prevention, Payments, Machine-Learning, and Artificial Intelligence.

🌐 Visit website 📡 Source on GitHub

1.5/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Company Fraud Prevention Payments Machine-Learning Artificial Intelligence Risk Management Security

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 1.5/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.3 / 9

Regulatory Posture 1.4 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Regulatory Posture applies to this provider. Its tags matched the Payments regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/fraugster: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Fraugster Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: fraugster
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/fraugster.png
name: Fraugster
description: Fraugster is a Berlin-based artificial intelligence company focused on payment fraud prevention and transaction
  risk scoring for online merchants, payment service providers, and acquirers. Its machine-learning platform was designed
  to analyze payments in real time to detect and block fraudulent transactions while reducing false declines and chargebacks.
  The company was surfaced through venture portfolio data (backed by Seedcamp) and added to the API Evangelist network. During
  this enrichment pass the company's public developer surface was not reachable to automated clients (the primary domain returns
  HTTP 403), so no API specifications, SDKs, or documentation could be harvested; only live infrastructure/domain-security
  signals were probed.
url: https://raw.githubusercontent.com/api-evangelist/fraugster/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- seedcamp
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Fraud Prevention
- Payments
- Machine-Learning
- Artificial Intelligence
- Risk Management
- Security
tags_raw:
- Company
- Fraud Prevention
- Payments
- Machine Learning
- Artificial Intelligence
- Risk Management
- Security
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/fraugster-domain-security.yml
- type: Website
  url: http://fraugster.com
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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