---
title: Interpretable ML for Creditor Recovery
url: https://www.ml-quant.com/papers/ssrn/4716003/
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 4716003
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4716003
featured: 2024-02-07
citations: unknown
topic: Risk, Credit & Banking
---


# Interpretable ML for Creditor Recovery

Interpretable machine learning methods excel over traditional models in finance, specifically in modeling corporate bond recovery rates.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4716003
- Identifier: SSRN 4716003
- Released: 2022-08-29
- First featured: Quant Letter No. 36 (2024-02-07): https://www.ml-quant.com/issues/2024-02-07/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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