---
title: Comparative Credit Risk Model Analysis
url: https://www.ml-quant.com/papers/ssrn/5204562/
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: SSRN 5204562
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5204562
featured: 2025-04-09
citations: unknown
topic: Risk, Credit & Banking
---


# Comparative Credit Risk Model Analysis

The analysis reveals that gradient boosting models, specifically CatBoost and LightGBM, are more effective than traditional models in assessing credit risk.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5204562
- Identifier: SSRN 5204562
- Released: 2025-04-03
- First featured: Quant Letter No. 92 (2025-04-09): https://www.ml-quant.com/issues/2025-04-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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