We study whether AI-extracted facial features from borrowers’ photos can serve as a scalable proxy for “soft” information missing from traditional credit models, such as conscientiousness, patience, and self-control. These traits influence financial behavior but are rarely captured in administrative data. Linking LinkedIn photos and employment and education records to voter registration and Experian data for over one million U.S. borrowers, we find that facial embeddings add significant predictive power for default risk beyond standard observables such as as credit scores, gender, and race. The incremental value is largest for younger, lower-income, and thin-file borrowers, where traditional credit scoring technology is least informative. A separate model mapping facial images to perceived Big Five personality traits reveals personality as one mechanism through which images proxy for soft information. These results suggest that facial embeddings capture stable behavioral traits absent from standard credit data, as well as perceived attributes that may influence how individuals are treated by others. While such models offer new insight into the role of personality and soft information in credit markets, their use in screening raises important concerns about fairness, privacy, and autonomy.