---
title: Unveiling Credit Risk Models
url: https://www.ml-quant.com/papers/repec/bde-revist-y-2022-i-11-n-4/
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:bde:revist:y:2022:i:11:n:4
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.bde.es%2Ff%2Fwebbde%2FGAP%2FSecciones%2FPublicaciones%2FInformesBoletinesRevistas%2FRevistaEstabilidadFinanciera%2F22%2F4_REF43_Black.pdf%3Bh%3Drepec%3Abde%3Arevist%3Ay%3A2022%3Ai%3A11%3An%3A4
featured: 2023-06-28
citations: unknown
topic: Risk, Credit & Banking
---


# Unveiling Credit Risk Models

Explainable AI helps understand credit risk in machine learning.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.bde.es%2Ff%2Fwebbde%2FGAP%2FSecciones%2FPublicaciones%2FInformesBoletinesRevistas%2FRevistaEstabilidadFinanciera%2F22%2F4_REF43_Black.pdf%3Bh%3Drepec%3Abde%3Arevist%3Ay%3A2022%3Ai%3A11%3An%3A4
- Identifier: RePEc:bde:revist:y:2022:i:11:n:4
- Released: 2022-07-25
- First featured: Quant Letter No. 5 (2023-06-28): https://www.ml-quant.com/issues/2023-06-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

## Related

- [Interpretable Learning in Credit Risk](https://www.ml-quant.com/papers/repec/eee-riibaf-v-65-y-2023-i-c-s0275531923000661/): Neural network with selective interpretability introduced for credit risk assessment, shallow model leads to better accuracy for specific data portions.
- [Interpretable ML for Creditor Recovery](https://www.ml-quant.com/papers/ssrn/4716003/): Interpretable machine learning methods excel over traditional models in finance, specifically in modeling corporate bond recovery rates.
- [Explainable AI for Credit Spread Changes Analysis](https://www.ml-quant.com/papers/repec/eee-finana-v-94-y-2024-i-c-s1057521924002473/): The research compares linear regression and machine learning for modeling credit spread changes, with machine learning showing superior performance due to its ability to handle complex non-linearities.
- [Machine Learning-Based Variable Selection for Clustered Credit Risk Modeling](https://www.ml-quant.com/papers/ssrn/4506537/): The piece proposes a machine learning-based method for selecting variables in clustered credit risk modeling, using the most influential risk drivers as clustering variables.
- [Double free boundary problem for defaultable corporate bond with credit rating migration risks and their asymptotic behaviors](https://www.ml-quant.com/papers/arxiv/2301.10898/): The paper presents a pricing model for a corporate bond with credit rating migration risk, proving the solution's existence, uniqueness, and regularity.
- [Study on Intelligent Forecasting of Credit Bond Default Risk](https://www.ml-quant.com/papers/arxiv/2305.12142/): Intelligent forecasting framework for default risk in China's bond market using ConvLSTM neural network.