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The Role of Alternative Data in Credit Risk Prediction

In: Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)

Author

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  • Zhaoyan Chen

    (Guangdong University of Technology, School of Economics)

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.

Suggested Citation

  • Zhaoyan Chen, 2025. "The Role of Alternative Data in Credit Risk Prediction," Advances in Economics, Business and Management Research, in: Junfeng Lu (ed.), Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024), pages 725-732, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-652-9_76
    DOI: 10.2991/978-94-6463-652-9_76
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