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Found 9 papers, 5 papers with code
Reducing the dimensionality and granularity in hierarchical categorical variables
We apply our methodology on a real dataset and find that the reduced hierarchy is an improvement over the original hierarchical structure and reduced structures proposed in the literature.
Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff
We compare in detail the performance of: a generalized linear model on binned input data, a gradient-boosted tree model, a feed-forward neural network (FFNN), and the combined actuarial neural network (CANN).
An engine to simulate insurance fraud network data
We therefore design a simulation machine that is engineered to create synthetic data with a network structure and available covariates similar to the real life insurance fraud data set analyzed in \'Oskarsd\'ottir et al. (2022).
Bridging the gap between pricing and reserving with an occurrence and development model for non-life insurance claims
The advantages of our proposed strategy are most compelling in the reinsurance illustration where large uncertainties in the best estimates originate from long reporting and settlement delays, low claim frequencies and heavy (even extreme) claim sizes.
Assessing the impact of the COVID-19 shock on a stochastic multi-population mortality model
To be compliant with the design and calibration strategy of the Li & Lee model, we have to transform the weekly mortality data collected in age buckets to yearly, age-specific observations.
Social network analytics for supervised fraud detection in insurance
Finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable.
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates
This results in a segmentation of the feature space with automatic variable selection.
A hierarchical reserving model for reported non-life insurance claims
We present the hierarchical reserving model as a modular framework for integrating a claim's history and claim-specific covariates into the development process.
Boosting insights in insurance tariff plans with tree-based machine learning methods
With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims.
