The Role of Alternative Data in Credit Risk Prediction
- DOI
- 10.2991/978-94-6463-652-9_76How to use a DOI?
- Keywords
- Alternative data; Credit risk prediction; Big data
- Abstract
With the fast development of FinTech and big data technology, alternative data is sweeping the world at a rapid pace and plays an important role in credit risk prediction. At present, many lending institutions can use alternative data, a non-traditional emerging data, to forecast credit risk. This paper aims to review the literature on the research topic ‘The role of alternative data in credit risk prediction’, finding the similarities and differences between these documents. The results show that personal psychological information, digital footprint and consumer credit information in alternative data can help banks and other lending institutions to predict credit risk, as well as greatly reduce and control the credit risk. However, the current research in this field has limited research data, small sample size, the interpretability and performance of the constructed models and algorithms need to be improved, and the factors of cost and profit are not considered. The purpose of this paper is to provide reference for the future research.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zhaoyan Chen PY - 2025 DA - 2025/02/24 TI - The Role of Alternative Data in Credit Risk Prediction BT - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024) PB - Atlantis Press SP - 725 EP - 732 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-652-9_76 DO - 10.2991/978-94-6463-652-9_76 ID - Chen2025 ER -