Understand how online fraud detection works
Learn how businesses identify suspicious transactions using fraud signals, risk scoring, validation rules, machine learning and real-time transaction analysis.
What is fraud detection?
Fraud detection is the process of evaluating transactions and user activity to identify patterns or signals that may indicate fraudulent behavior. In e-commerce, detection systems can analyze multiple data points in real time before a transaction is completed.
Fraud Detection
Identify suspicious transactions by analyzing relevant signals, historical patterns and risk indicators.
Fraud Prevention
Use detection results, rules and business processes to reduce fraudulent orders and unnecessary losses.
Risk-Based Decisions
Use a combination of risk signals and rules to determine whether a transaction should be approved, reviewed or rejected.
How does fraud detection identify suspicious transactions?
Fraud detection examines transaction details and evaluates them against known risk indicators and historical patterns. Instead of relying on a single warning sign, it combines multiple signals to assess whether an order may require further investigation.
Collect Transaction Signals
Gather relevant information such as IP address, email, payment details, billing and shipping addresses, and device data to understand the transaction context.
Identify Risk Indicators
Check for suspicious characteristics such as anonymous proxies, address mismatches, blacklisted information, disposable emails and unusual transaction activity.
Analyze Historical Patterns
Compare transaction characteristics against historical activity and known fraud patterns to detect unusual behavior that may indicate increased risk.
Assess Transaction Risk
Combine validation findings and risk indicators into a fraud score that helps merchants identify suspicious transactions requiring additional attention.
From transaction data to a fraud decision
A modern fraud detection workflow combines multiple signals instead of relying on a single indicator. The more relevant transaction information available for analysis, the more context a fraud screening system can evaluate.
Capture
Collect transaction and customer information at checkout.
Validate
Check IP, email, phone, address, payment and other signals.
Analyze
Evaluate risk indicators, rules, blacklist data and patterns.
Score
Generate an overall indication of transaction risk.
Decide
Apply business rules to approve, review or reject.
What information can be used to identify risk?
Fraud detection can combine signals from different parts of a transaction. FraudLabs Pro's Screen Order API supports a broad set of transaction inputs and returns corresponding validation results.
What is a fraud score?
A fraud score summarizes the assessed risk of a transaction. FraudLabs Pro returns a score from 1 to 100, where 1 represents the lowest risk and 100 the highest risk.
The score can be used together with validation rules to help determine the appropriate transaction action.
Actual decisions depend on configured validation rules and transaction context.
Combine automated analysis with business rules
Fraud detection is not only about generating a score. Rules let merchants define how specific risk conditions should be handled, while machine learning can help identify patterns that may not be captured by fixed rules alone.
Validation Rules
Configure conditions that trigger actions such as Review or Reject based on your business requirements.
Machine Learning
FraudLabs Pro uses transaction history and merchant approval, rejection and blacklisting actions to learn fraud patterns.
Blacklist Data
Blacklist information can provide another signal when an IP address, email address or device has been associated with reported fraud.
Where fraud detection is used
Different businesses face different fraud patterns. The underlying detection concepts can be applied across online transactions, customer accounts and digital commerce workflows.
E-commerce transactions
Screen online orders before fulfillment and identify transactions that require additional review.
Chargeback risk
Use transaction and customer signals to identify suspicious orders that may contribute to fraud losses.
Account & identity risk
Evaluate user, email, phone and device information as part of a broader fraud screening process.
Ready to put fraud detection into practice?
Choose the path that matches your role: explore product capabilities, start an integration or go directly to the API.
Go deeper with FraudLabs Pro
The concept guide explains the ideas. These existing resources provide the product, implementation and technical depth.
Fraud detection FAQs
Clear answers to common questions about online fraud detection, risk scoring and transaction screening.
What is online fraud detection?
Online fraud detection is the process of analyzing transactions and related information to identify signals that may indicate fraudulent activity. It can use data such as IP address, email, phone, address, payment and device information.
How does fraud detection work?
A fraud detection workflow typically collects transaction signals, validates them, analyzes risk indicators, calculates or assigns a risk level, and applies business rules to determine whether the transaction should be approved, reviewed or rejected.
What is a fraud score?
A fraud score is a numerical representation of assessed transaction risk. FraudLabs Pro returns a score from 1 to 100, with 1 representing the lowest risk and 100 the highest risk.
What signals are used in fraud detection?
Common signals include IP address and location, proxy indicators, email, phone, billing and shipping information, payment and card information, device data, account information and transaction context.
Is a high fraud score always a fraudulent transaction?
A high score indicates higher assessed risk, but the appropriate action depends on the transaction context and the merchant's configured validation rules. A review workflow can be used when additional verification is appropriate.
What is the difference between fraud detection and fraud prevention?
Fraud detection focuses on identifying suspicious activity or risk. Fraud prevention uses those findings together with rules, controls and operational processes to reduce the likelihood or impact of fraudulent transactions.
Can fraud detection work with an e-commerce platform?
Yes. FraudLabs Pro provides integrations for supported e-commerce platforms as well as a REST API for custom web solutions.
How can I start using FraudLabs Pro?
You can begin with the FraudLabs Pro Quick Start Guide, which covers API integration, configuring validation rules and fraud score settings, whitelisting legitimate customers and testing your configuration.
Explore FraudLabs Pro fraud detection
Screen online transactions and evaluate multiple fraud signals with a free API key.