ipasis

Prevent Affiliate Fraud & Stop Fake Conversions

Protect your referral program from manipulation. IPASIS detects bot traffic, proxy abuse, and fake accounts used to game affiliate commissions — ensuring you only pay for real, legitimate referrals.

Common Affiliate Fraud Tactics

🤖

Bot Click Farms

Fraudulent affiliates use bots to generate thousands of fake clicks and conversions, draining your commission budget with zero real customers.

🎭

Self-Referral Abuse

Affiliates create multiple accounts with VPNs and disposable emails to refer themselves, collecting commissions on their own purchases.

🔄

Cookie Stuffing

Malicious scripts inject affiliate cookies without user knowledge, claiming credit for conversions they didn't drive.

How IPASIS Detects Affiliate Fraud

1

IP Reputation Analysis

Flags IPs with a history of affiliate fraud, click fraud, or belonging to known click farms and bot networks.

2

Proxy & VPN Detection

Identifies users hiding behind VPNs, proxies, or Tor to create multiple accounts and exploit self-referral programs.

3

Datacenter IP Detection

Real users don't shop from AWS or DigitalOcean. Flags conversions from hosting providers commonly used for bot farms.

4

Email Validation

Detects disposable emails and newly created domains used to bypass duplicate account checks and abuse referral bonuses.

Implementation Example

Validate affiliate-driven signups and conversions before crediting commissions:

// Affiliate conversion handler (Node.js example)
import axios from 'axios';

app.post('/api/track-conversion', async (req, res) => {
  const { 
    affiliateId, 
    email, 
    orderId, 
    amount 
  } = req.body;
  
  const userIP = req.ip;

  try {
    // Validate conversion with IPASIS
    const ipasisResponse = await axios.post(
      'https://api.ipasis.com/check',
      {
        ip: userIP,
        email: email
      },
      {
        headers: {
          'Authorization': `Bearer ${process.env.IPASIS_API_KEY}`,
          'Content-Type': 'application/json'
        }
      }
    );

    const { trustScore, signals } = ipasisResponse.data;

    // Determine fraud risk level
    let fraudRisk = 'low';
    let approveCommission = true;

    if (trustScore < 30) {
      fraudRisk = 'high';
      approveCommission = false;
      
      await logFraudulentConversion({
        affiliateId,
        email,
        ip: userIP,
        orderId,
        amount,
        trustScore,
        signals,
        blocked: true
      });

      // Block commission payment
      return res.status(200).json({
        tracked: true,
        commissionApproved: false
      });
    }

    if (trustScore < 60) {
      fraudRisk = 'moderate';
      approveCommission = false; // Hold for review
    }

    // Record conversion with fraud metadata
    await recordAffiliateConversion({
      affiliateId,
      email,
      orderId,
      amount,
      userIP,
      trustScore,
      fraudRisk,
      commissionApproved: approveCommission,
      ipasisSignals: signals,
      timestamp: new Date()
    });

    // Auto-approve high-quality conversions (trustScore >= 60)
    if (approveCommission) {
      const commission = calculateCommission(amount, affiliateId);
      
      await creditAffiliateCommission({
        affiliateId,
        conversionId: orderId,
        amount: commission,
        status: 'approved'
      });
    } else {
      // Queue for manual review
      await queueForReview({
        affiliateId,
        conversionId: orderId,
        reason: 'Low trust score',
        trustScore
      });
    }

    res.json({
      tracked: true,
      commissionApproved: approveCommission,
      fraudRisk
    });

  } catch (error) {
    console.error('IPASIS validation failed:', error);
    
    // Fallback: track conversion but hold commission
    await recordAffiliateConversion({
      affiliateId,
      email,
      orderId,
      amount,
      commissionApproved: false,
      flaggedForReview: true,
      error: 'IPASIS_CHECK_FAILED'
    });

    res.json({
      tracked: true,
      commissionApproved: false
    });
  }
});

💡 Best Practice: Implement a two-tier system: auto-approve high-trust conversions (>70), hold moderate risk (40-70) for review, and auto-reject high risk (<40). Review patterns monthly to adjust thresholds.

ROI of Fraud Prevention

$47k

Average annual savings per program

83%

Reduction in fraudulent conversions

4min

Average integration time

Stop Paying for Fake Conversions

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