---
title: Explainable ML Methods for Actuarial Problems
url: https://www.ml-quant.com/papers/repec/gam-jmathe-v-11-y-2023-i-14-p-3088-d-1193020/
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:gam:jmathe:v:11:y:2023:i:14:p:3088-:d:1193020
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F14%2F3088%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A14%3Ap%3A3088-%3Ad%3A1193020
featured: 2023-07-19
citations: unknown
topic: Risk, Credit & Banking
---


# Explainable ML Methods for Actuarial Problems

The article examines different explainable AI methods for data-based insurance issues, highlighting the need for accurate and understandable machine-learning solutions.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F14%2F3088%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A14%3Ap%3A3088-%3Ad%3A1193020
- Identifier: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3088-:d:1193020
- Released: 2023-07-19
- First featured: Quant Letter No. 8 (2023-07-19): https://www.ml-quant.com/issues/2023-07-19/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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