What caused the sudden spike in failed transactions for Airwallex's Borderless Cards in the APAC region last week?

Introduction

The sudden spike in failed transactions for Airwallex's Borderless Cards in the APAC region last week presents a critical issue that demands immediate attention and thorough analysis. As we delve into this problem, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the regional specificity, I'm wondering about localized factors. Could you provide more details on which specific countries within APAC were most affected by this spike in failed transactions?

Why it matters: This helps narrow down potential regional issues or regulatory changes. Expected answer: A list of 2-3 most affected countries. Impact on approach: Focusing on country-specific factors if isolated, or broader regional issues if widespread.

• Considering the timing, I'm curious about any recent system updates. Were there any significant changes to the Borderless Cards platform or related systems in the days leading up to this spike?

Why it matters: Recent changes often correlate with sudden performance issues. Expected answer: Information on recent updates or confirmation of no major changes. Impact on approach: Investigating specific changes if present, or looking at external factors if no updates occurred.

• Thinking about transaction types, I'm wondering about the nature of the failed transactions. Can you provide a breakdown of the types of transactions that were failing (e.g., online purchases, ATM withdrawals, point-of-sale transactions)?

Why it matters: Different transaction types may point to specific system components or partner integrations. Expected answer: A percentage breakdown of failed transaction types. Impact on approach: Focusing on specific transaction flows or partner relationships based on the most affected types.

• Considering user segments, I'm curious about the customer base affected. Was this issue widespread across all user types, or did it disproportionately affect certain segments (e.g., business vs. personal accounts, high-volume vs. low-volume users)?

Why it matters: Segmentation can reveal patterns and help prioritize impact. Expected answer: Information on affected user segments or confirmation of uniform impact. Impact on approach: Tailoring the investigation to specific user journeys or exploring broader system issues based on the affected segments.