This paper examines whether a socially embedded soft trait, systematically omitted from AI-automated credit decisions, can predict financial outcomes in fully automated lending environments, and through which economic channel. We develop theoretically and examine empirically a labor-market competitiveness mechanism in which facial trustworthiness can serve as a soft signal under information asymmetry, shaping income growth and repayment capacity. Leveraging a unique proprietary dataset from a nationwide FinTech platform in China with AI-based underwriting that explicitly excludes facial information, we link loan performance to employment histories and construct trustworthiness scores using computer-vision algorithms. We find that borrowers with more trustworthy facial appearances experience greater upward job mobility and significantly lower default risk, conditional on rich hard information. A one-standard-deviation increase in facial trustworthiness is associated with a 4.6 percent reduction in default relative to the sample mean. The effect is strongest among borrowers with limited formal credentials, consistent with facial trustworthiness substituting for hard-to-observe labor-market signals. Our findings identify an economically grounded channel through which biometric appearance predicts financial resilience and highlight limits of algorithmic models that omit socially embedded information.