Geographic Ratemaking With Spatial Embeddings

Self Cite

Unstructured data are a promising new source of information that insurance companies may use to understand their risk portfolio better and improve the customer experience. However, these novel data sources are difficult to incorporate into existing ratemaking frameworks due to the size and format of the unstructured data. This paper proposes a framework to use street view imagery within a generalized linear model. To do so, we use representation learning to extract an embedding vector containing useful information from the image. This embedding is dense and low dimensional, making it appropriate to use within existing ratemaking models. We find that there is useful information included in street view imagery to predict the frequency of claims for certain types of perils. This model can be used as in a ratemaking framework but also opens the door to future empirical research on attempting to extract which characteristics within the image leads to increased or decreased predicted claim frequencies. Throughout, we discuss the practical difficulties (technical and social) of using this type of data for insurance pricing.

Section: Unsupervised Representation Learning Frameworkmentioning

confidence: 99%

“…That framework has been applied to spatial data in Blier-Wong et al. (2022). Also, the authors of Lee et al.…”

Section: Unsupervised Representation Learning Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A representation-learning approach for insurance pricing with images

Blier-Wong,

Lamontagne,

Marceau

2024

Self Cite

“…The benefits of autoencoders without noise in supervised and unsupervised learning tasks have been demonstrated by Gao and Wüthrich (2018), Hainaut (2018), Rentzmann and Wüthrich (2019), Blier-Wong et al . (2021, 2022), Miyata and Matsuyama (2022) and Grari et al . (2022).…”

Section: Introductionmentioning

confidence: 98%

The use of autoencoders for training neural networks with mixed categorical and numerical features

Delong

Kozak

2023

We focus on modelling categorical features and improving predictive power of neural networks with mixed categorical and numerical features in supervised learning tasks. The goal of this paper is to challenge the current dominant approach in actuarial data science with a new architecture of a neural network and a new training algorithm. The key proposal is to use a joint embedding for all categorical features, instead of separate entity embeddings, to determine the numerical representation of the categorical features which is fed, together with all other numerical features, into hidden layers of a neural network with a target response. In addition, we postulate that we should initialize the numerical representation of the categorical features and other parameters of the hidden layers of the neural network with parameters trained with (denoising) autoencoders in unsupervised learning tasks, instead of using random initialization of parameters. Since autoencoders for categorical data play an important role in this research, they are investigated in more depth in the paper. We illustrate our ideas with experiments on a real data set with claim numbers, and we demonstrate that we can achieve a higher predictive power of the network.

“…In Meng et al (2022), the authors propose a supervised driving risk scoring convolutional neural network (CNN) model that uses telematics car driving data to improve automobile insurance claims frequency prediction. Blier-Wong et al (2020) propose a convolutional regional autoencoder model for generating geographical risk encodings using CNNs. The resulting encodings, which aim to replace the traditional territory variable, proved beneficial for risk-related regression tasks.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data

Duval,

Boucher,

Pigeon

2024

We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the combinedd actuarial neural network (CANN) framework proposed by Wüthrich and Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.