Use cases

Overview

These examples show how businesses use Protect to reduce fraud exposure, manage risk at scale, and keep legitimate customers from being incorrectly blocked.

Block transactions from high-risk card countries

An e-commerce business notices a spike in disputed card transactions originating from specific countries. They create a block rule targeting those card country codes to automatically decline matching transactions before they complete.

block if card_country_id: ['NG', 'RO']

Disputed transactions from those origins drop immediately. They continue monitoring the blocked list to check for false positives and adjust the rule as needed.

Flag high-value transactions for manual review

A marketplace that handles large one-time purchases wants human oversight on any transaction above ₱50,000. They create a review rule so those transactions are held in the queue before the order is processed.

review if payment_amount_gte: 50000

Their fraud team reviews each flagged transaction, approves legitimate orders, and closes the review. Chargebacks on large orders fall significantly.

Combine risk score and geography for targeted blocking

A subscription platform is seeing fraudulent card attempts from a specific region with high risk scores. Instead of blocking all transactions from that region, they create a rule that only blocks when both conditions are met — reducing the chance of blocking legitimate customers.

block if risk_score_gte: 700 AND card_country_id: ['US']

This keeps their false positive rate low while still catching the fraud pattern they identified.

Allow trusted low-risk transactions to skip review

A business with a large volume of low-value, low-risk transactions finds the default review rules are creating unnecessary queue backlogs. They create an allow rule so that transactions below a certain risk score proceed automatically.

allow if risk_score_lte: 399

Their fraud team can now focus on genuinely suspicious transactions instead of reviewing clean ones.