Design a 'Trusted Buyer' Reputation Score for E-commerce

To design a Trusted Buyer Score for Amazon, I would first align with the goal of maximizing merchant profitability by reducing friction for low-risk buyers while blocking bad actors. The primary metric would be the reduction in merchant chargeback costs and return processing fees. For data inputs, I would aggregate three layers: Account Health (age, email validity), Transactional History (return rate relative to category average, frequency of refunds), and Behavioral Patterns (device consistency, IP reputation). A simple rule-based system won't suffice; I'd propose a machine learning model using XGBoost that ingests these features to output a probability score of future fraud or abusive returns. Crucially, the score must be dynamic. If a trusted buyer suddenly exhibits risky behavior, their score drops instantly. We would then apply this score to improve the merchant experience by allowing high-scoring buyers to bypass certain verification steps, receive faster payouts for their orders, or access exclusive 'No-Questions-Asked' return windows which actually reduces administrative overhead for merchants handling fewer disputes. Conversely, low scores trigger stricter identity checks. This approach ensures we protect merchant margins without alienating our most loyal Prime members, directly supporting Amazon's obsession with customer delight and operational efficiency.