---
title: Credit Risk Management
url: https://www.ml-quant.com/papers/repec/ids-ijmpra-v-17-y-2024-i-5-p-509-521/
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:ids:ijmpra:v:17:y:2024:i:5:p:509-521
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D140863%3Bh%3Drepec%3Aids%3Aijmpra%3Av%3A17%3Ay%3A2024%3Ai%3A5%3Ap%3A509-521
featured: 2024-09-25
citations: unknown
topic: Risk, Credit & Banking
---


# Credit Risk Management

The article examines the application of AI in creating credit scoring models by banks to assess the creditworthiness of borrowers.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D140863%3Bh%3Drepec%3Aids%3Aijmpra%3Av%3A17%3Ay%3A2024%3Ai%3A5%3Ap%3A509-521
- Identifier: RePEc:ids:ijmpra:v:17:y:2024:i:5:p:509-521
- Released: 2024-09-25
- First featured: Quant Letter No. 67 (2024-09-25): https://www.ml-quant.com/issues/2024-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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