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Credit Scoring Prediction for Small and Medium-Sized Enterprises Based on Alternative Data and Gradient Boosting Algorithms

Shu Chen

Information Resources Management Journal2026https://doi.org/10.4018/irmj.400759article
AJG 1ABDC C
Weight
0.50

What the paper says

In today's complex economic landscape, small and medium-sized enterprises (SMEs) are crucial drivers of growth, yet traditional credit scoring models often fail to capture their true creditworthiness because they are limited by narrow data sources and poor data adaptability. With the rise of big data and fintech, alternative data opens a richer avenue for SME credit assessment. This study leverages real-world, publicly available data, which includes operational behavior, supply chain interactions, and online transactions, to help build a more inclusive and forward-looking credit scoring framework for SMEs. The authors enhance the model's nonlinear fitting and feature representation capabilities by employing gradient boosting algorithms to significantly improve credit risk prediction accuracy. They compare the performance of various machine learning models and discuss trade-offs between predictive power, generalizability, and interpretability. The results offer financial institutions a dynamic, multidimensional risk assessment tool able to provide actionable insights for policy and practice.

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https://doi.org/https://doi.org/10.4018/irmj.400759

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@article{shu2026,
  title        = {{Credit Scoring Prediction for Small and Medium-Sized Enterprises Based on Alternative Data and Gradient Boosting Algorithms}},
  author       = {Shu Chen},
  journal      = {Information Resources Management Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/irmj.400759},
}

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Credit Scoring Prediction for Small and Medium-Sized Enterprises Based on Alternative Data and Gradient Boosting Algorithms

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.