Acquiring Fraud: Detection and Prevention Strategies

Acquiring fraud targets merchants and payment processors rather than cardholders. This type of fraud can have significant financial impact and requires specialized detection strategies. This article explores acquiring fraud patterns, detection methods, and prevention approaches.

What is Acquiring Fraud?

Acquiring fraud occurs when fraudsters target the merchant or payment processor side of transactions. Unlike issuing fraud (which targets cardholders), acquiring fraud involves:

Merchant Fraud: Fraudulent merchants processing transactions
Chargeback Fraud: Unauthorised chargebacks and disputes
Friendly Fraud: Legitimate transactions disputed as fraudulent
Merchant Account Takeover: Unauthorised access to merchant accounts
Collusive Merchants: Merchants working with fraudsters

Types of Acquiring Fraud

1. Merchant Fraud

What it is: Fraudulent merchants created to process stolen payment methods or launder money.

Characteristics:

Rapid account creation and closure
High transaction volumes
Unusual transaction patterns
Minimal business verification
High chargeback rates

Red Flags:

New merchant with high transaction volume
Business model doesn't match transaction patterns
Poor website quality or legitimacy
Geographic mismatches
Rapid account lifecycle

Detection:

Merchant onboarding verification
Transaction pattern analysis
Chargeback monitoring
Business model validation
Network analysis

2. Chargeback Fraud

Types:

a. True Fraud Chargebacks

Legitimate fraud, but merchant bears cost
Cardholder didn't authorise transaction
Merchant may have weak authentication

b. Friendly Fraud

Legitimate transaction disputed as fraudulent
Cardholder claims non-receipt or unauthorised
Difficult to prove legitimacy

c. Merchant Error Chargebacks

Processing errors by merchant
Incorrect transaction handling
Customer service issues

Impact:

Financial losses for merchants
Increased processing costs
Reputation damage
Account restrictions or closure

3. Merchant Account Takeover

How it works:

Fraudsters gain access to merchant account
Modify account settings
Process fraudulent transactions
Withdraw funds before detection

Attack Vectors:

Credential theft
Phishing attacks
Weak authentication
Social engineering
Insider threats

Detection:

Unusual account activity
Settings changes
New withdrawal methods
Login anomalies
Transaction pattern changes

4. Collusive Merchants

What it is: Legitimate merchants working with fraudsters to process fraudulent transactions.

Characteristics:

Legitimate business front
Processing transactions for fraudsters
Receiving percentage of fraudulent transactions
Difficult to detect initially

Indicators:

High chargeback rates
Unusual transaction patterns
Customer complaints
Geographic mismatches
Network connections to known fraud

Detection Strategies

Merchant Onboarding Verification

Key Checks:

Business registration verification
Identity verification of business owners
Website and business model validation
Financial history review
Risk scoring

Tools:

Business verification services
Identity verification platforms
Credit checks
Sanctions screening
PEP (Politically Exposed Person) checks

Transaction Monitoring

Merchant-Level Metrics:

Transaction volume trends
Chargeback rates
Refund rates
Average transaction amounts
Geographic distribution
Time-based patterns

Anomaly Detection:

def detect_merchant_anomalies(merchant_data):
    """
    Detect anomalies in merchant transaction patterns
    """
    anomalies = []
    
    # Sudden volume increase
    if merchant_data['volume_growth'] > 500:
        anomalies.append('sudden_volume_increase')
    
    # High chargeback rate
    if merchant_data['chargeback_rate'] > 0.05:
        anomalies.append('high_chargeback_rate')
    
    # Unusual transaction patterns
    if merchant_data['avg_amount'] > merchant_data['typical_amount'] * 3:
        anomalies.append('unusual_amounts')
    
    return anomalies

Chargeback Analysis

Key Metrics:

Chargeback rate (chargebacks / transactions)
Chargeback reason codes
Chargeback trends over time
Win/loss rates
Time to chargeback

Analysis:

Identify high-risk merchants
Detect chargeback fraud patterns
Understand root causes
Develop prevention strategies

Network Analysis

Identifying Fraud Networks:

Connections between merchants
Shared characteristics
Transaction flows
Common fraud indicators

Techniques:

Graph database analysis
Community detection
Link analysis
Pattern recognition

Prevention Strategies

Strong Onboarding

Verification Requirements:

Comprehensive business verification
Identity verification of owners
Financial history checks
Business model validation
Risk assessment

Ongoing Monitoring:

Regular account reviews
Transaction monitoring
Chargeback tracking
Compliance checks

Risk-Based Approach

Risk Scoring:

Merchant risk scores
Transaction risk scores
Dynamic risk adjustment
Automated decisioning

Controls:

Reserve requirements
Transaction limits
Settlement delays
Enhanced monitoring

Technology Solutions

Authentication:

Multi-factor authentication for merchant accounts
Device fingerprinting
Behavioural analysis
Risk-based authentication

Monitoring:

Real-time transaction monitoring
ML-based fraud detection
Anomaly detection
Network analysis

Data:

Merchant intelligence
Threat intelligence
Shared fraud databases
Industry collaboration

Case Study: Detecting Fraudulent Merchant

Scenario

A new merchant account processes $500,000 in transactions in the first week, with 80% chargeback rate.

Detection Process

Step 1: Onboarding Review

Business verification incomplete
Website appears fraudulent
Owner identity verification failed
High risk score

Step 2: Transaction Analysis

All transactions are card-not-present
Transactions from multiple countries
Unusual transaction amounts
Rapid transaction velocity

Step 3: Chargeback Analysis

Chargebacks start within 48 hours
High chargeback rate (80%)
Multiple fraud reason codes
Pattern indicates stolen cards

Step 4: Network Analysis

Connections to other fraudulent merchants
Shared characteristics
Coordinated fraud network

Action Taken

Immediate account suspension
Transaction blocking
Funds held
Investigation initiated
Network analysis expanded

Outcome

Prevented additional losses, identified fraud network, and improved detection rules for similar patterns.

Best Practices

Merchant Management

Thorough Onboarding: Comprehensive verification and risk assessment
Ongoing Monitoring: Continuous transaction and chargeback monitoring
Risk-Based Controls: Appropriate controls based on risk level
Regular Reviews: Periodic account reviews and updates
Clear Communication: Transparent policies and expectations

Chargeback Management

Prevention: Strong authentication and verification
Monitoring: Real-time chargeback tracking
Analysis: Understanding root causes
Response: Effective dispute management
Improvement: Learning from chargebacks

Collaboration

Industry Sharing: Sharing fraud intelligence
Cross-functional: Working with compliance, legal, and operations
Merchant Education: Helping merchants prevent fraud
Technology Partners: Leveraging fraud prevention tools

Regulatory Considerations

Compliance Requirements

PCI DSS: Payment card data security
AML/KYC: Merchant verification requirements
Data Protection: GDPR and privacy regulations
Consumer Protection: Chargeback and dispute regulations

Reporting

Suspicious activity reporting
Regulatory notifications
Industry reporting
Internal reporting

Metrics and KPIs

Key Metrics

Chargeback Rate: Chargebacks / Total transactions
Fraud Rate: Fraudulent transactions / Total transactions
False Positive Rate: Incorrectly flagged merchants
Detection Time: Time to identify fraud
Prevention Rate: Fraud prevented / Total fraud attempts

Dashboard Metrics

Merchant risk distribution
Chargeback trends
Fraud patterns
Top risk merchants
Prevention effectiveness

Future Trends

Emerging Threats

AI-Powered Fraud: Sophisticated fraud using AI
Cryptocurrency Integration: Using crypto for fraud
Cross-Platform Attacks: Coordinated attacks
Regulatory Changes: Evolving compliance requirements

Evolving Defences

Advanced ML: More sophisticated detection
Real-time Processing: Faster detection and response
Industry Collaboration: Enhanced fraud sharing
Automated Response: Automated fraud prevention

Conclusion

Acquiring fraud requires specialized detection and prevention strategies. Effective fraud management involves:

Strong merchant onboarding and verification
Continuous transaction and chargeback monitoring
Network analysis to identify fraud rings
Risk-based controls and responses
Collaboration across teams and industry

The key is to balance fraud prevention with merchant support, ensuring legitimate merchants can operate while preventing fraud. This requires analytical thinking, technical skills, and a deep understanding of merchant behaviour and fraud patterns.