For Feedzai's machine learning models, should we emphasize interpretability for regulatory compliance or maximize predictive accuracy for fraud prevention?

Introduction

The trade-off between interpretability and predictive accuracy in Feedzai's machine learning models for fraud prevention presents a critical challenge. We must balance regulatory compliance needs with maximizing fraud detection capabilities. I'll analyze this trade-off through multiple lenses, considering business impact, technical feasibility, and user experience.

Step 1

Clarifying Questions (3 minutes)

• Based on recent regulatory trends, I'm thinking interpretability might be a growing concern. Could you share any specific regulatory pressures or deadlines we're facing?

Why it matters: Helps prioritize interpretability vs. accuracy based on urgency Expected answer: Increasing scrutiny, potential fines for "black box" models Impact on approach: Would emphasize interpretability if regulatory pressure is high

• Considering our business model, I assume we have different tiers of customers with varying needs. Can you clarify our customer segments and their priorities regarding fraud prevention vs. explainability?

Why it matters: Allows tailoring of solution to different customer needs Expected answer: Mix of large enterprises (more concerned with compliance) and smaller businesses (prioritize accuracy) Impact on approach: Might lead to a segmented solution strategy

• From a user perspective, I'm curious about the current false positive rate. How is it impacting customer satisfaction and operational costs?

Why it matters: Helps balance accuracy improvements against potential increase in false positives Expected answer: Moderate false positive rate causing some customer friction Impact on approach: Would influence the trade-off between accuracy and interpretability

• Technically, I'm wondering about our current model architecture. How modular is it, and can we implement interpretability layers without a complete overhaul?

Why it matters: Determines feasibility and resource requirements for adding interpretability Expected answer: Somewhat modular, but significant work required for full interpretability Impact on approach: Would affect timeline and resource allocation for implementation

• Regarding timeline, how urgent is this decision? Are we looking at an immediate change or a phased approach over quarters?

Why it matters: Influences the scope and depth of the solution Expected answer: Phased approach preferred, but some changes needed within 6 months Impact on approach: Would shape the prioritization of quick wins vs. long-term restructuring