Riskified

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Subscribe to events via webhooks G

Agent access

Build against official SDKs G

Agent access

Run the product headlessly / in CI for automation G

Agent access

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Rely on versioned APIs with a documented deprecation policy G

Api quality

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Issue scoped/least-privilege API credentials for an agent G

Agent access

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

Use an official CLI G

Agent access

Test against a sandbox environment without touching production data G

Api quality

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

Scoring api

Chargeback responses are automated — evidence compiled from order, delivery, and session data and submitted to the issuer without manual copy-paste C

Representment

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

Psp coverage

Define rules that trigger actions automatically on events G

Author custom rules that combine model scores, velocity counters, list matches, and transaction attributes into allow, block, or review decisions C

Rule authoring

An agent can read my fraud posture and manage rules and lists programmatically — propose a velocity rule, update a blocklist — with human approval gates C

Agent operations

Every score comes with its top risk factors — why this transaction looks risky — not just an opaque number C

Explainability

Export all of my data in open formats and leave G

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

Case review

Prevent my data from being used to train AI models G

Self-host the core product G

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

Sca

There are maintained integrations for my commerce stack — Shopify, Salesforce Commerce, BigCommerce, and the major PSPs — not just a raw API C

Integrations

Protection extends beyond checkout — account takeover, fake account creation, promo and policy abuse are scored and managed in the same system C

Abuse coverage

Shift fraud liability to the vendor — a chargeback guarantee that reimburses approved-then-disputed orders, with clear coverage terms C

Guarantee

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

Custom signals

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

Score actions

My review decisions and confirmed fraud outcomes feed back into the model and rules, so the system learns from every case we work C

Feedback loop

An agent can work the review queue — pull flagged cases with their context, summarize the evidence, and recommend a decision for a human to confirm C

Agent triage

Backtest a rule against my historical traffic before deploying it, seeing exactly what it would have blocked, flagged, and cost C

Backtesting

Choose where my data is stored (region/residency) G

Control data retention and deletion G

Control where fraud data lives and how long it's kept — regional residency options and retention controls that survive a privacy review C

Residency

Do everything through the API that I can do in the UI G

Maintain allow and block lists — emails, cards, devices, IPs — and velocity limits, managed through the dashboard and programmatically C

Lists

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

Model evaluation

Opt out of telemetry and usage tracking G

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

Agent identity

Perform bulk operations across many items at once G

Read the product's source under an open license G

Review work is a team workflow — assignment, escalation, SLAs, and a decision audit trail that shows who approved what and why C

Team workflows

Schedule recurring jobs or workflows G

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

Network effects

See the numbers that matter — dispute rate, false-positive rate, approval-rate lift, review workload — and export them for the board C

Outcome reporting

The product distinguishes malicious bots from legitimate AI buying agents, so agent-driven purchases aren't blanket-blocked as fraud C

Agent detection

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

Builtin ai

Version, review, and roll back my automations G