Is it better for Naked Insurance to invest in AI-driven risk assessment or human underwriters for more accurate pricing?

Introduction

The trade-off between investing in AI-driven risk assessment or human underwriters for more accurate pricing at Naked Insurance is a critical decision that could significantly impact the company's future. This scenario touches on the core of insurance operations: risk assessment and pricing. I'll analyze this trade-off by examining the potential impacts on accuracy, scalability, and customer experience, while considering the long-term implications for Naked Insurance's business model.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking about the current state of Naked Insurance's risk assessment process. Could you provide an overview of how risk assessment and pricing are currently handled?

Why it matters: Understanding the baseline helps identify improvement areas and potential challenges. Expected answer: A mix of automated systems and human underwriters, with some inefficiencies. Impact on approach: Would help determine the scale of change required and potential resistance points.

• Business Context: Based on industry trends, I assume improving pricing accuracy is a key strategic priority. How does this initiative align with Naked Insurance's current business goals and revenue model?

Why it matters: Ensures the solution aligns with overall business strategy. Expected answer: Critical for profitability and market competitiveness. Impact on approach: Would influence resource allocation and implementation timeline.

• User Impact: I'm curious about our customer segments. Which user groups would be most affected by changes in our risk assessment approach?

Why it matters: Helps tailor the solution to meet specific user needs and expectations. Expected answer: Varied impact across different risk profiles and insurance products. Impact on approach: Would inform personalization strategies and communication plans.

• Technical Feasibility: Considering AI implementation, what's our current data infrastructure like? Do we have the necessary data quality and quantity to support AI-driven risk assessment?

Why it matters: Determines the feasibility and potential effectiveness of an AI solution. Expected answer: Decent data infrastructure, but some gaps in data quality or coverage. Impact on approach: Would influence the timeline and resources needed for AI implementation.

• Resource Allocation: I'm thinking about team capacity. What's the current size and expertise of our underwriting team versus our data science team?

Why it matters: Helps assess the feasibility of each option and potential training needs. Expected answer: Established underwriting team, growing data science capabilities. Impact on approach: Would impact the balance between AI and human-driven solutions.