Diagnose Return Eligibility Scoring Bugs

You work on an e-commerce service that determines return eligibility. For every return request, the service calculates a risk score based on signals like:

• the customer's previous return count,
• the item's value,
• the age of the customer's account,
• the item category and any historical fraud or abuse indicators. When the risk score is too high, the item is supposed to be marked as non-returnable. Recently, the system has wrongly let some high-risk items be returned. In an AI-assisted debugging round, walk through how you would investigate the problem and identify six concrete defect categories that could produce these false approvals. For each defect, cover how you would detect it, fix it, and keep it from recurring. Overview: This question assesses debugging and root-cause analysis skills in scoring logic, data integrity, feature engineering, and system reliability for return-eligibility services.