How can we explain the 30% drop in accuracy of EXL's machine learning models for insurance underwriting over the last month?

Introduction

The 30% drop in accuracy of EXL's machine learning models for insurance underwriting over the last month is a critical issue that demands immediate attention. This significant decline in model performance could have far-reaching consequences for the company's core business operations, customer satisfaction, and overall market position. To address this complex problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent change in the data pipeline. Has there been any modification to the data ingestion or preprocessing steps in the past month?

Why it matters: Changes in data handling could significantly impact model accuracy. Expected answer: Yes, there was a recent update to the data preprocessing pipeline. Impact on approach: If confirmed, we'd focus on investigating the data pipeline changes and their effects.

• Considering the scale of the drop, I'm wondering about the model's input data. Have there been any significant changes in the insurance market or customer behavior recently?

Why it matters: External factors could be affecting the data distribution the model was trained on. Expected answer: No major market shifts, but there's been a slight increase in high-risk policy applications. Impact on approach: We'd need to analyze if the model is struggling with these new high-risk cases.

• Given the specificity of the 30% drop, I'm curious about our monitoring systems. Are we confident in the accuracy of this measurement, and has the method for calculating model accuracy remained consistent?

Why it matters: Ensures we're addressing a real issue and not a measurement anomaly. Expected answer: The measurement system is reliable and unchanged. Impact on approach: We'd focus on the model and data, rather than questioning the metrics themselves.

• Thinking about the model lifecycle, I'm wondering about recent updates. Has there been any retraining or fine-tuning of the model in the past month?

Why it matters: Recent model changes could explain the sudden drop in accuracy. Expected answer: The model was retrained two weeks ago with new data. Impact on approach: We'd scrutinize the retraining process and the new data used.