This is the second volume.

  • Properties of Alternative Data for Fairer Credit Risk Predictions
    Jung Youn Lee, Joonhyuk Yang, (2):1−27, 2024.
    Abstract

    In the consumer lending market, women tend to have lower access to credit than men, despite evidence suggesting that women are better at repaying their debts. This study explores the potential impact of leveraging alternative data, which traditionally has not been used by financial institutions, on credit risk predictions between men and women. By leveraging unique data on individuals’ credit card default behaviors and their purchase behaviors at a supermarket, we simulate a credit card issuer’s credit scoring process. In the absence of supermarket data, the algorithm’s predictive accuracy for women is about 2.3% lower than that for men. We then integrate data from each of the 410 product markets within the supermarket into the algorithm and measure the changes in the gender gap in predictive accuracy. We find a wide variation in both direction and magnitude in the incremental gender gap, ranging from -142% to 70% compared to the baseline. These findings highlight that leveraging alternative data from a non-financial domain can lead to fairer credit outcomes, but only under certain conditions. We characterize the conditions by identifying two data properties: the capacity to proxy gender and the relative amount of creditworthiness signals data provide for each gender.
    [PDF] [bib]

  • OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
    Jingyang Zhang, Jingkang Yang, Pengyun Wang, Haoqi Wang, Yueqian Lin, Haoran Zhang, Yiyou Sun, Xuefeng Du, Yixuan Li, Ziwei Liu, Yiran Chen, Hai Li, (3):1−32, 2024.
    Abstract

    Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods, the evaluation inconsistencies present challenges for tracking the progress in this field. OpenOOD v1 initiated the unification of the OOD detection evaluation but faced limitations in scalability and scope. In response, this paper presents OpenOOD v1.5, a significant improvement from its predecessor that ensures accurate and standardized evaluation of OOD detection methodologies at large scale. Notably, OpenOOD v1.5 extends its evaluation capabilities to large-scale data sets (ImageNet) and foundation models (e.g., CLIP and DINOv2), and expands its scope to investigate full-spectrum OOD detection which considers semantic and covariate distribution shifts at the same time. This work also contributes in-depth analysis and insights derived from comprehensive experimental results, thereby enriching the knowledge pool of OOD detection methodologies. With these enhancements, OpenOOD v1.5 aims to drive advancements and offer a more robust and comprehensive evaluation benchmark for OOD detection research.
    [PDF] [bib]