---
title: ML for ReTakaful Contributions
url: https://www.ml-quant.com/papers/repec/idn-jimfjn-v-9-y-2023-i-3g-p-511-532/
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:idn:jimfjn:v:9:y:2023:i:3g:p:511-532
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fjimf-bi.org%2Findex.php%2FJIMF%2Farticle%2Fview%2F1681%2F948%3Bh%3Drepec%3Aidn%3Ajimfjn%3Av%3A9%3Ay%3A2023%3Ai%3A3g%3Ap%3A511-532
featured: 2023-10-12
citations: unknown
topic: Risk, Credit & Banking
---


# ML for ReTakaful Contributions

The article employs machine learning to find the best ReTakaful contributions model for Morocco's Islamic insurance sector, showcasing the algorithms' potential in calculating appropriate contributions.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fjimf-bi.org%2Findex.php%2FJIMF%2Farticle%2Fview%2F1681%2F948%3Bh%3Drepec%3Aidn%3Ajimfjn%3Av%3A9%3Ay%3A2023%3Ai%3A3g%3Ap%3A511-532
- Identifier: RePEc:idn:jimfjn:v:9:y:2023:i:3g:p:511-532
- Released: 2023-10-12
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

## Related

- [Differential quantile-based sensitivity in discontinuous models](https://www.ml-quant.com/papers/arxiv/2310.06151/): Discontinuous Model Sensitivity Analysis: The article discusses a framework for understanding complex computational models, focusing on discontinuities and discrete input variables, and applies it to risk analysis of compound risk models and reinsurance credit risk.
- [Mean-Field Libor Market Model and Valuation of Long Term Guarantees](https://www.ml-quant.com/papers/arxiv/2310.09022/): The article introduces a numerical asset-liability management model based on the multi-dimensional mean-field Libor market model, which can calculate future discretionary benefits in line with Solvency II regulation, using public life insurance data.
- [Neural Networks for Insurance Pricing with Frequency and Severity Data: A Benchmark Study from Data Preprocessing to Technical Tariff](https://www.ml-quant.com/papers/arxiv/2310.12671/): The research evaluates different models for insurance claim frequency and severity data, presenting combined actuarial neural networks (CANNs) that merge baseline predictions with a neural network correction, and recommends using global surrogate models for practical deployment.
- [On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach](https://www.ml-quant.com/papers/ssrn/4580817/): A paper suggests using a simulation approach with mortality-linked securities and stochastic mortality rates to manage capital risk in the insurance industry and meet regulatory capital requirements.
- [Ensemble distributional forecasting for insurance loss reserving](https://www.ml-quant.com/papers/arxiv/2206.08541/): The paper introduces a framework for combining multiple stochastic loss reserving models, which performs better than traditional strategies and equally weighted ensembles, taking into account the full distributional properties of the ensemble.
- [Law-invariant return and star-shaped risk measures](https://www.ml-quant.com/papers/arxiv/2310.19552/): The paper introduces new characterizations for law-invariant star-shaped functionals, demonstrating their wide use in finance, insurance, and probability scenarios.