> For the complete documentation index, see [llms.txt](https://docs.payshield.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.payshield.ai/payshield-fraudcheck/overview.md). # Overview TxShield's Fraud Management system evaluates transactions for risk using a configurable set of local rules and third-party scoring providers. Fraud checks can run in two modes: embedded within a payment transaction or as a standalone check independent of any payment.\ \ Two Processing Modes\ 1\. Embedded Fraud Check (Payment-Triggered)\ Fraud checks run automatically as part of processing a `PAYMENT`, `PREAUTH`, or `AUTH` transaction. If the transaction's threat score exceeds the configured threshold, the payment is declined before authorization is attempted. No separate API call is needed — the fraud evaluation is built into the transaction flow.\ 2\. Standalone Fraud Check\ A fraud check can also be submitted on its own — without processing a payment — using the `FRAUDCHECK` action. This allows merchants to evaluate a cardholder's risk profile before committing to a transaction, or to use TxShield's fraud scoring as a decision layer in their own workflow.\ A standalone check accepts the same inputs as a payment (card details, billing/shipping address, email, IP, cart data) but produces only a risk result — no charge is made.\[8:05 PM]Fraud Checks: Built-in vs. External\ Built-in checks (processed locally and immediately):
* Velocity — repeated identical transactions within a short window * BIN validation and issuing country check * GeoIP — IP location vs. billing/shipping address * Transaction limits — daily/monthly caps by IP, email, or card * Chargeback and decline history * Card and IP whitelists/blacklists * Repeat transaction detection across card, email, or phone External risk provider checks (real-time third-party scoring):
* IP reputation, proxy/VPN/TOR detection * Email validity and disposable email detection * Phone number intelligence and risk scoring * Identity verification across name, address, phone, and email * Device fingerprinting and behavioral signals * Order-level fraud scoring --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.payshield.ai/payshield-fraudcheck/overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.