Fraud Detection & Prevention at apis.io/use-cases/fraud-detection/ resolves 29 providers and 723 APIs — catching fraudulent signups, payments, and account activity through risk scoring, device and behavioral signals, chargeback defense, and account-takeover prevention.
It is one of the highest-scoring use-cases on the network. Eight of its providers sit in the exemplar band.
The cohort
| Provider | Score | Band | APIs |
|---|---|---|---|
| Adyen | 77.8 | Exemplar | 202 |
| Amazon Rekognition | 77.4 | Exemplar | 10 |
| Amazon Neptune | 76.4 | Exemplar | 29 |
| Amazon SageMaker | 76.4 | Exemplar | 9 |
| IPinfo | 75.6 | Exemplar | 17 |
| Amazon Connect | 75.3 | Exemplar | 22 |
| Sift | 73.4 | Exemplar | 6 |
| Abstract API | 70.6 | Exemplar | 14 |
| Amazon Fraud Detector | 69.3 | Strong | 7 |
| Have I Been Pwned | 61.5 | Strong | 8 |
| AT&T | 61.9 | Strong | 28 |
A build path
Fraud detection is a layered problem, and the cohort maps onto the layers cleanly.
Signal collection first. IPinfo (75.6) for network and geolocation context — is this request coming from a residential IP in the country the account claims? Have I Been Pwned (61.5) for credential-exposure checks at signup. Abstract API (70.6) for email and phone validity. These are cheap, fast, and catch a surprising share of low-effort fraud before anything expensive runs.
Identity and device next. Amazon Rekognition (77.4) where document or face verification is warranted — onboarding a seller, opening an account. This is the expensive tier; you gate it behind the cheap signals.
Scoring on top. Sift (73.4) is the pure-play here: six APIs, exemplar band, nothing but risk scoring. Amazon Fraud Detector (69.3) if you are already in that ecosystem and want managed models. Amazon SageMaker (76.4) if you are training your own on your own labels.
Graph for rings. Amazon Neptune (76.4) is the non-obvious inclusion and the most interesting one. Serious fraud is rarely a single bad actor; it is a ring sharing devices, addresses, cards, and IPs across many accounts. That is a graph traversal, not a per-transaction score, and it is the layer most teams add far too late.
Payments-side signals last. Adyen (77.8) carries fraud tooling inside its 202-API surface — BinLookup, Disputes, and the risk data attached to the payment itself. If you are already processing through it, that context is free.
The observation
The pure-play fraud vendors score high on small surfaces. Sift: six APIs, 73.4. That is the pattern across every specialist category in the catalog — when the API is the company, it gets described properly. Fraud detection has an unusually high concentration of such companies, which is why the use-case scores the way it does.
Takeaway
29 providers, 723 APIs, and a clean layering from cheap signals to expensive verification to graph analysis. Start with IPinfo and Abstract API on every signup, add a scoring layer, and reach for Neptune when you realize you are fighting rings rather than individuals.
Browse the use-case at apis.io/use-cases/fraud-detection/.