Paper presents a method for geographic ratemaking using spatial embeddings.
problem Lack of historical loss data in areas with high exposures.
method Construct spatial features within a complex representation model and use them as inputs to a predictive model.
result Predictions have smaller bias and variance than other spatial interpolation models.
Paper compares MICE-based methods to deep generative models for synthetic data in ratemaking.
problem Limited access to high-quality data for actuarial ratemaking.
method Benchmarked MICE-based models against VAEs and CTA-GANs.
result MICE-based models preserve marginal distributions and multivariate relationships better than deep models.
LLMs help automate extraction of actuarial variables from unstructured claims data.
problem Manual processing of unstructured claims data is time-consuming and inconsistent.
method Two-stage processing architecture using LLMs, modular Python pipeline.
result LLM-based extraction achieved high accuracy and practical actuarial value.
The risk premium of a policy is the sum of the pure premium and the risk loading. In the classification ratemaking process, generalized linear models are usually used to calculate pure premiums, and various premium principles are applied to derive the risk loadings. No matter which premium principle is used, some risk …
Generative adversarial networks create synthetic insurance datasets from confidential originals.
problem Difficulty in accessing or sharing confidential insurance datasets for research.
method Design and use of three GAN architectures tailored for multi-categorical insurance data.
result MC-WGAN-GP synthesizes the best data, CTGAN is easiest to use, and MNCDP-GAN ensures differential privacy.
Paper aims to make insurance models more understandable.
problem Lack of transparency in machine learning models for insurance pricing.
method Proposes a framework for explaining machine learning models in insurance ratemaking.
result Illustrates the proposed framework with a case study.
It is illustrated a methodology to compute the pure premium for the automobile insurance (claim frequency and severity) using generalized linear models. It is obtained the pure premium for the partial damage loss cover (PPD) using a set of automobile insurance policies with an exposition of a year. It is found that the…
Paper analyzes cyber risk classifications for forecasting performance.
problem Lack of effective out-of-sample forecasting performance in current cyber risk classifications.
method Rolling window analysis using threshold weighted scoring functions.
result Dynamic and impact-based cyber risk classifiers outperform others in forecasting future cyber risk losses.
mSHAP explains predictions of two-part models, improving fairness and interpretability.
problem Interpreting predictions from two-part models, especially in insurance.
method mSHAP: a method for computing SHAP values of two-part models using the SHAP values of individual models.
result mSHAP is exponentially faster than kernelSHAP for computing approximate SHAP values.
Paper proposes a risk index combining frequency and severity of abnormal driving patterns.
problem Assessing driver risk based on telematics data.
method Combines frequency of abnormal driving patterns with severity quantified through tail rarity.
result Developed a risk index that enables reliable discrimination and ranking of drivers.