What factors are contributing to the increased error rate in Abrigo's Validate automated loan decisioning system this month?

Introduction

Abrigo's Validate automated loan decisioning system is experiencing an increased error rate this month, potentially impacting loan approvals and customer satisfaction. To address this critical issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both immediate fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• I'm noticing the issue is specific to this month. Has there been any recent system update or change in loan criteria?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: A system update was implemented last month. Impact on approach: If confirmed, I'd focus on the update's impact on error rates.

• Looking at the error rate increase, I'm wondering about the magnitude. Can you share the percentage increase in errors compared to previous months?

Why it matters: The scale of the issue informs the urgency of our response. Expected answer: A 20-30% increase in error rates. Impact on approach: A significant increase would prioritize immediate action over long-term solutions.

• Considering user segments, are all loan types equally affected, or is the increase concentrated in specific categories?

Why it matters: Segmentation helps narrow down potential causes. Expected answer: Higher error rates in small business loans. Impact on approach: I'd focus on small business loan processing specifics if confirmed.

• Given the automated nature of the system, I'm curious about any changes in data sources. Have there been any modifications to the data inputs or integrations recently?

Why it matters: Data quality directly impacts automated decision accuracy. Expected answer: No changes to data sources reported. Impact on approach: If no changes, I'd shift focus to internal system processes rather than data inputs.