---
title: Machine Learning in Credit Scoring
url: https://www.ml-quant.com/papers/repec/eee-finsta-v-73-y-2024-i-c-s157230892400069x/
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:eee:finsta:v:73:y:2024:i:c:s157230892400069x
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS157230892400069X%3Bh%3Drepec%3Aeee%3Afinsta%3Av%3A73%3Ay%3A2024%3Ai%3Ac%3As157230892400069x
featured: 2024-08-07
citations: unknown
topic: Risk, Credit & Banking
---


# Machine Learning in Credit Scoring

A study reveals that machine learning models using unconventional data are more efficient in predicting credit losses and defaults, particularly during economic crises.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS157230892400069X%3Bh%3Drepec%3Aeee%3Afinsta%3Av%3A73%3Ay%3A2024%3Ai%3Ac%3As157230892400069x
- Identifier: RePEc:eee:finsta:v:73:y:2024:i:c:s157230892400069x
- Released: 2024-08-07
- First featured: Quant Letter No. 60 (2024-08-07): https://www.ml-quant.com/issues/2024-08-07/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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