Merchant Fraud and Abuse Detection

Uncover hidden exposure

Catch coordinated fraud

Improve model precision

Understanding Merchant Fraud

Merchant fraud encompasses deceptive practices by sellers to exploit payment systems, evade policies, or steal from customers and payment processors. Unlike customer fraud (stolen cards, account takeovers), merchant fraud involves businesses deliberately misrepresenting their operations to process illicit transactions or avoid compliance.

The Cost of Merchant Fraud

• Direct Losses: Chargebacks, refunds, and fraudulent payouts
• Regulatory Penalties: Card network fines for high chargeback rates and policy violations
• Operational Costs: Investigation time, legal fees, account recovery
• Reputational Damage: Brand harm from facilitating scams and counterfeit sales

Common Merchant Fraud Patterns

1. Fake Product Schemes

Merchants create professional-looking storefronts selling products they never ship, or ship drastically different items than advertised.

Common indicators:

• Limited seller verification imagery
• Prices significantly below market rates
• New accounts with unexpectedly high volumes
• Generic product descriptions
• Incomplete business contact information

2. Rapid Account Cycling

Fraudsters create merchant accounts, process high volumes quickly, then disappear before chargebacks arrive.

Common indicators:

• High transaction volumes immediately after account approval
• Shared infrastructure across multiple merchant accounts
• Duplicate contact information or payment details
• Minimal or recently established web presence

3. Prohibited Product Sales

Merchants sell prohibited items while misrepresenting their business category to avoid detection.

Common indicators:

• Category classifications don't match actual products
• Product descriptions indicating prohibited categories
• Suspicious communication patterns
• High-risk shipping patterns

4. Refund Fraud Schemes

Merchants collude with customers to file fraudulent chargebacks, keeping both the product and refund.

Common indicators:

• Elevated chargeback rates
• Patterns in chargeback sources
• Inconsistencies between reviews and chargeback data
• Suspicious shipping information

5. Price Manipulation

Merchants dramatically inflate prices for low-value items to maximize fraudulent payouts.

Common indicators:

• Prices significantly above market rates
• Synthetic or misleading product imagery
• Price changes coinciding with volume spikes
• Inconsistent product descriptions and valuations

SafetyKit's Fraud Detection System

Multi-Signal Analysis

SafetyKit combines transaction data, business intelligence, and behavioral patterns:

1. Transaction Monitoring: Analyzes volume patterns, transaction characteristics, and chargeback rates
2. Business Intelligence: Reviews merchant operations, content, and public information
3. Network Analysis: Identifies shared infrastructure and coordinated fraud operations
4. Behavioral Profiling: Compares merchant patterns against known fraud schemes

Automated Risk Scoring

Each merchant receives comprehensive risk scores based on:

• MCC classification accuracy
• Transaction pattern anomalies
• Website legitimacy assessment
• Product compliance violations
• Historical behavior and account age
• Network connections to known fraud

Enterprise Features

Team Collaboration

• Multi-user access with role-based permissions
• Investigation notes and internal communication
• Case assignment and workload distribution
• Audit trails for all decisions and actions

Customizable Policies

• Configure risk thresholds per business line
• Custom policy definitions for specific compliance requirements
• Allowlists and blocklists for special merchant categories
• Integration with internal risk scoring models

Customer Success Story

Underwriting team discovered $22M in unknown exposure:

An underwriting team faced mounting credit losses with little insight. Luckily, SafetyKit had already been integrated by their T&S team. SafetyKit discovered $22M in unknown exposure, with 10X greater accuracy than previous automated models.

Results:

• 10% credit model precision → 85% credit model precision
• $22M in previously unknown exposure identified
• Proactive risk mitigation before losses materialized
• Integration with existing T&S workflows

Scale and Efficiency

Process thousands of merchant investigations daily with consistent quality. SafetyKit eliminates the manual work of web research, policy cross-referencing, and report generation—freeing compliance teams to focus on edge cases and strategic risk management.