Stop fraud before it reaches your business.

RevCent protects ecommerce businesses with Sentinel Anti-Fraud, RevCent’s free in-house fraud prevention system that evaluates visitors, customers, cards, velocity, order context, and previous fraud intelligence before risky payments reach the processor.

Sentinel Anti-Fraud

Prevent fraud before payment authorization.

Sentinel is RevCent’s in-house anti-fraud system. It runs before the payment is sent to a gateway, so high-risk attempts can be stopped before they create processing fees, gateway pressure, lost inventory, disputes, or damaged merchant history.

Signals Sentinel reads
IP
Visitor intelligenceIP reputation, request velocity, location, device, user agent, proxy, and behavioral signals.
VIS
Tracking visitor contextRevCent compares checkout behavior, attribution, metadata, and visitor history against the purchase attempt.
PAY
Payment contextCustomer, card, cart, product, shipping, prior declines, prior approvals, and prior fraud signals are evaluated together.
Sentinel decision engine Pre-gateway risk check
91/100 Risk
DecisionBlock before gateway
ReasonVelocity + visitor mismatch
Stored contextFraud signals saved
What RevCent can do
Allow clean ordersLegitimate transactions can continue through the payment path without unnecessary friction.
!
Stop risky attemptsSuspicious requests can be blocked before authorization, processing fees, disputes, or fulfillment exposure.
EV
Attach the evidenceRisk details stay attached for review, reporting, tuning, customer support, and future fraud checks.
Multiple Layers

Fraud is stopped at multiple levels.

Fraud rarely shows up as one obvious signal. RevCent evaluates the request from several angles before deciding whether a payment should continue, be blocked, or be reviewed.

01
Request layer Rate limits and IP intelligence. Sentinel checks request volume, IP reputation, location, proxy patterns, and whether the request looks like automation or card testing.
IPvelocityautomation
02
Visitor layer Tracking visitor validation. RevCent compares the purchase attempt against visitor data, source metadata, device behavior, checkout path, and attribution context.
visitordevicemetadata
03
Business layer Customer and order context. Sentinel evaluates the customer, card, products, sale history, shipping details, previous failures, and prior fraud intelligence.
customerorderhistory
Fraud Memory

RevCent remembers previous fraud.

A fraud attempt should make the business smarter. RevCent stores fraud context so previous attacks, suspicious patterns, blocked attempts, and risk signals can influence what happens next.

Previous attempts are not forgotten when the same visitor, IP pattern, customer profile, card behavior, or checkout pattern appears again.
Fraud context becomes operational because the signals can be attached to sales, customers, visitors, payments, reporting, and support review.
Future checks get smarter as RevCent keeps a record of what was blocked, why it was blocked, and what pattern was involved.
Detect Suspicious behavior appears.

A payment attempt shows mismatched visitor data, repeated attempts, risky request timing, or prior fraud similarity.

Store RevCent saves the fraud context.

The fraud reason, visitor context, payment details, customer relationship, and decision output stay connected.

Reuse Future decisions use the memory.

Later attempts can be evaluated against known behavior instead of being treated like a brand-new event.

Card Testing Protection

Stop card testing before it burns your gateway.

Card testing attacks can create thousands of rapid payment attempts. Sentinel is built to recognize those patterns quickly and stop the attempts before they become gateway events.

01
Attack begins Rapid attempts hit checkout. Fraudsters try many stolen card numbers in a short window, often using automated traffic and repeated checkout behavior.
rapidautomated
02
Sentinel identifies Velocity and pattern risk spikes. RevCent checks attempt volume, visitor mismatch, IP behavior, card changes, and repeated failure patterns before gateway authorization.
velocitypattern
03
Fraud blocked Processor never sees the attack. Risky attempts can be blocked before they create authorization fees, gateway pressure, decline spikes, or merchant account damage.
blockedremembered
Case Studies

Real attacks become better protection.

The value of anti-fraud is not just blocking one suspicious payment. It is reducing processing damage, preserving merchant account health, and turning attack patterns into reusable intelligence.

Case 01 2,500 stolen cards blocked in minutes.

Single card-testing event. Five-minute window. No attempts sent to the processor.

A client store was targeted by fraudsters attempting to test approximately 2,500 stolen cards. Sentinel detected the rapid card-testing pattern and prevented the attempts from reaching the processor before they could create transaction fees, gateway pressure, or merchant account risk.

After the attack failed, the fraudsters stopped within minutes. RevCent also preserved the fraud intelligence so future attempts and similar patterns can be evaluated faster for every protected account.

Case 02 $360,000 in fraud prevented in one month.

Total prevented amount calculated across one client account. None sent to the processor.

RevCent analyzed a month of fraudulent payment attempts for one client account and calculated that roughly $360,000 in suspicious payment activity was stopped before entering normal processing paths.

Sentinel reviewed each attempt before processor authorization using layered checks like request risk, visitor validation, payment context, customer history, and fraud memory. That helped protect the business from transaction fees, chargeback exposure, and merchant account damage.