Using postal codes as a primary factor in credit scoring raises concerns of geographic and socioeconomic bias. Postal codes can serve as proxies for race, ethnicity, or income level-potentially violating fair lending laws and ethical guidelines.
"Auditors should flag models that rely on proxies for protected attributes. Postal codes are high-risk features that may inadvertently lead to redlining or other discriminatory practices." A, B, and C are more justifiable under fair lending guidelines. Thus, D presents the highest concern.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Bias and Fairness in Financial AI Models"