How might Riskified enhance its Account Secure product to provide more accurate risk scores for new account creations?

Introduction

To enhance Riskified's Account Secure product and provide more accurate risk scores for new account creations, we need to dive deep into the current product, user behavior, and market dynamics. I'll structure my approach by first clarifying key aspects of the problem, then analyzing user segments and pain points, generating solutions, and finally evaluating and prioritizing these solutions with appropriate metrics.

Step 1

Clarifying Questions

• Looking at the product context, I'm thinking Account Secure might be targeting e-commerce platforms or financial institutions. Could you help me understand who the primary users of this product are and what specific use cases they're addressing?

Why it matters: This will help us tailor our solutions to the most relevant user needs. Expected answer: E-commerce platforms are the primary users, focusing on reducing fraud during account creation. Impact on approach: Would focus on e-commerce-specific fraud patterns and integration with existing platforms.

• Considering user behavior, I'm curious about the current accuracy rate of the risk scores. Could you share some insights on the false positive and false negative rates we're currently seeing?

Why it matters: This will help us identify which aspect of the risk scoring needs the most improvement. Expected answer: False positive rate is around 5%, while false negative rate is about 2%. Impact on approach: Would prioritize reducing false positives to improve user experience if these were the actual figures.

• Thinking about the product lifecycle, where does Account Secure currently stand? Are we looking at early adoption, rapid growth, or a mature product seeking optimization?

Why it matters: This will influence whether we focus on feature expansion or optimization of existing capabilities. Expected answer: The product is in a growth phase with increasing adoption but facing scalability challenges. Impact on approach: Would prioritize scalability and performance improvements over new feature development.

• Considering external factors, how has the competitive landscape evolved recently? Are there any new entrants or technologies disrupting the fraud detection space?

Why it matters: This will help us identify areas where we need to innovate to stay ahead. Expected answer: There's increasing competition from AI-driven solutions offering real-time fraud detection. Impact on approach: Would explore incorporating advanced AI and machine learning techniques into our solution.