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Credit evaluation solutions for social groups with poor services in financial inclusion: A technical forecasting method

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  • Zhang, Lifeng
  • Chao, Xiangrui
  • Qian, Qian
  • Jing, Fuying

Abstract

Financial inclusion aims to provide financial services at an affordable cost to low-income groups in need. However, the lack of effective credit evaluation information for such groups has hindered the innovative development of financial inclusion in the banking industry. This study proposes a slack constrained matrix factorisation model to supplement missing credit information. The method fills in missing data with known data in groups of similar credit behaviours. We empirically analyse the performance of this method in a supplemented credit information matrix and a sparse credit information matrix. We use actual credit data of farmers and herdsmen in extremely poor areas and small, medium and micro enterprises in National Equities Exchange and Quotations, China. This study concludes that the proposed credit evaluation methods based on sparse credit information can effectively improve the performance of traditional credit classification algorithms.

Suggested Citation

  • Zhang, Lifeng & Chao, Xiangrui & Qian, Qian & Jing, Fuying, 2022. "Credit evaluation solutions for social groups with poor services in financial inclusion: A technical forecasting method," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522004255
    DOI: 10.1016/j.techfore.2022.121902
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    Cited by:

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    2. Mingtao Yu & Aiguo Yan, 2022. "Can Digital Finance Accelerate the Digital Transformation of Companies? From the Perspective of M&A," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    3. Xinmin Liu & Xinjiang Wang & Wencheng Yu, 2023. "Opportunity or Challenge? Research on the Influence of Digital Finance on Digital Transformation of Agribusiness," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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