---
title: Credit Risk Prediction for SMEs Using ML
url: https://www.ml-quant.com/papers/repec/wly-mgtdec-v-45-y-2024-i-4-p-2393-2414/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:wly:mgtdec:v:45:y:2024:i:4:p:2393-2414
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Fmde.4130%3Bh%3Drepec%3Awly%3Amgtdec%3Av%3A45%3Ay%3A2024%3Ai%3A4%3Ap%3A2393-2414
featured: 2024-05-28
citations: unknown
topic: Risk, Credit & Banking
---


# Credit Risk Prediction for SMEs Using ML

The FS-RS-ML framework, using machine learning to predict credit risk in supply chain finance for small and medium-sized enterprises, has proven superior in tests using Chinese data.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Fmde.4130%3Bh%3Drepec%3Awly%3Amgtdec%3Av%3A45%3Ay%3A2024%3Ai%3A4%3Ap%3A2393-2414
- Identifier: RePEc:wly:mgtdec:v:45:y:2024:i:4:p:2393-2414
- Released: 2024-05-28
- First featured: Quant Letter No. 51 (2024-05-28): https://www.ml-quant.com/issues/2024-05-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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