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Found 7 papers, 0 papers with code
Conditional expectation network for SHAP
A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP).
Isotonic Recalibration under a Low Signal-to-Noise Ratio
Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts.
A Discussion of Discrimination and Fairness in Insurance Pricing
This is particularly the case in insurance pricing where protected policyholder characteristics are not allowed to be used for insurance pricing.
Model selection with Gini indices under auto-calibration
The Gini index does not give a strictly consistent scoring rule in general.
A multi-task network approach for calculating discrimination-free insurance prices
Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination.
LocalGLMnet: interpretable deep learning for tabular data
Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models.
Assessing asset-liability risk with neural networks
We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period.
