Mobile Banking Analytics
Without User IDs

🏦 Why Banking Analytics Is Different

Mobile banking apps operate at the intersection of financial services regulation, payment card industry requirements, and data protection law. While an e-commerce app might only consider GDPR for analytics, a banking app potentially faces:

• • PCI DSS for any cardholder data environment
• • GDPR for EU user data processing
• • LGPD for Brazilian user data
• • PDPA for Singapore and Southeast Asia
• • Central bank regulations (BCB in Brazil, MAS in Singapore)

Each framework has its own requirements for consent, data minimization, cross-border transfers, and breach notification. Traditional analytics that collect device IDs, build user profiles, and store data indefinitely create complexity across all of them.

💳 PCI DSS v4.0: Script Management Requirements

PCI DSS v4.0 introduced significant new requirements for client-side scripts on payment pages. While often discussed in web contexts, these requirements directly impact mobile banking apps that use WebViews for payment flows—a common pattern for 3-D Secure verification, bill payments, and credit card applications.

🌍 Global Privacy Regulations

Beyond PCI DSS, banking apps must navigate regional privacy frameworks. Each emphasizes data minimization and purpose limitation—principles that conflict with traditional "collect everything" analytics.

⚠️ Risks of Traditional Analytics in Banking

Traditional analytics architectures create specific vulnerabilities for financial applications:

🏗️ Session-Based Analytics Architecture

Respectlytics helps developers avoid collecting personal data in the first place. Our motto is Return of Avoidance (ROA)—the best way to handle sensitive data is to never collect it.

{
  "event_name": "transfer_initiated",    // What happened
  "session_id": "[hashed_rotating_id]",  // RAM-only, daily salt
  "timestamp":  "2026-01-16T10:30:00Z",   // When it occurred
  "platform":   "ios",                      // iOS, Android, Web
  "country":    "US"                        // From transient IP lookup
}

📊 What You Can Measure

Session-based analytics provide meaningful product insights without requiring persistent user identification:

🔧 Implementation Guide

1. Design Privacy-Safe Event Names

Event names should be descriptive enough to provide insight without custom properties that might contain financial data:

// ✅ Good: Descriptive event names
"login_successful"
"transfer_initiated"
"balance_viewed"
"card_activated"
"biometric_enabled"
"payment_completed"

// ❌ Blocked: Custom properties with financial data
"transaction" + {amount: 150.00}      // API rejects
"user" + {account_id: "12345"}      // API rejects

2. SDK Integration

import RespectlyticsSwift

// Configure at app launch
Respectlytics.configure(apiKey: "your-api-key")

// Track banking events
Respectlytics.track("login_successful")
Respectlytics.track("balance_viewed")
Respectlytics.track("transfer_initiated")
Respectlytics.track("payment_completed")

import com.respectlytics.sdk.Respectlytics

// Configure in Application class
Respectlytics.configure(context, "your-api-key")

// Track banking events
Respectlytics.track("login_successful")
Respectlytics.track("account_opened")
Respectlytics.track("card_activated")

3. Payment Flow Considerations

For PCI DSS considerations, you may want to limit analytics during sensitive payment flows:

// Track entry to payment flow
Respectlytics.track("payment_flow_started")

// ... sensitive card entry happens in WebView ...
// No tracking during card data entry

// Track completion after returning from payment
Respectlytics.track("payment_completed")
// or
Respectlytics.track("payment_failed")

⚖️ Trade-offs and Limitations

Session-based analytics require accepting specific trade-offs. This architecture is designed for product teams who prioritize data minimization over cross-session user profiling: