Towards Explainability of Machine Learning Models in Insurance Pricing

Kuo, Kevin; Lupton, Daniel E.

doi:10.48550/arxiv.2003.10674

Cited by 3 publications

(4 citation statements)

References 20 publications

(19 reference statements)

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“…Henckaerts et al (2021) capitalized on ML methods to price non-life insurance products based on the frequency and severity of claims; their results are superior to the ones produced by the traditionally employed generalized linear models (GLMs). Kuo and Lupton (2020) explained that the wider adoption of ML techniques (over GLMs) in property and casualty insurance pricing depends very much on their reduced (perceived) transparency. They recommend increased interpretability to overcome this hurdle.…”

Section: Reservingmentioning

confidence: 99%

Machine Learning in Forecasting Motor Insurance Claims

Poufinas,

Gogas,

Papadimitriou

et al. 2023

Risks

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Accurate forecasting of insurance claims is of the utmost importance for insurance activity as the evolution of claims determines cash outflows and the pricing, and thus the profitability, of the underlying insurance coverage. These are used as inputs when the insurance company drafts its business plan and determines its risk appetite, and the respective solvency capital required (by the regulators) to absorb the assumed risks. The conventional claim forecasting methods attempt to fit (each of) the claims frequency and severity with a known probability distribution function and use it to project future claims. This study offers a fresh approach in insurance claims forecasting. First, we introduce two novel sets of variables, i.e., weather conditions and car sales, and second, we employ a battery of Machine Learning (ML) algorithms (Support Vector Machines—SVM, Decision Trees, Random Forests, and Boosting) to forecast the average (mean) insurance claim per insured car per quarter. Finally, we identify the variables that are the most influential in forecasting insurance claims. Our dataset comes from the motor portfolio of an insurance company operating in Athens, Greece and spans a period from 2008 to 2020. We found evidence that the three most informative variables pertain to the new car sales with a 3-quarter and 1-quarter lag and the minimum temperature of Elefsina (one of the weather stations in Athens) with a 3-quarter lag. Among the models tested, Random Forest with limited depth and XGboost run on the 15 most informative variables, and these exhibited the best performance. These findings can be useful in the hands of insurers as they can consider the weather conditions and the new car sales among the parameters that are considered to perform claims forecasting.

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Section: Reservingmentioning

confidence: 99%

Machine Learning in Forecasting Motor Insurance Claims

Poufinas,

Gogas,

Papadimitriou

et al. 2023

Risks

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“…In short, insurance analytics offers scalable optimisation and high-value commercial solutions to IVCs and business models. Still, EU regulation is seeking to govern the use by the steering industry to more equitable, transparent and explainable (Kuo and Lupton 2020) uses of data analytics (EIOPA 2021;Mullins et al 2021; van den Boom 2021).…”

Section: The Importance Of Explainability In Insurance Analyticsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Insurance

Owens

Sheehan

Mullins

et al. 2022

Risks

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Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.

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“…The study focusing on risk factors used in rate regulation is different from the predictive modeling of insurance loss conducted by an individual company, where all relevant risk factors are being considered. This difference allows us to focus on predictive models that are more interpretable, such as multiple linear regression models, generalized linear models, generalized additive models and other interpretable machine learning models [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…One may need to see that, among all factors considered in the model, what the measures in terms of their importance to model buildings are, mainly when factors are categorical and consist of many different factor levels. Therefore, the investigation of suitable approaches to measuring variable importance in insurance pricing has become an emerging research area and has attracted significant attention in machine learning for insurance [11,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions

Xie

Luo

2022

Mathematics

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Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in statistical inference, results obtained from predictive modeling serve as statistical evidence for the decision making of the underlying problem and discovering the functional relationship between the response variable and the predictors. As a result of this, the variable importance measures become an essential aspect of helping to better understand the contributions of predictors to the built model. In this work, we focus on the study of using generalized linear models (GLM) for the size of loss distributions. In addition, we address the problem of measuring the importance of the variables used in the GLM to further evaluate their potential impact on insurance pricing. In this regard, we propose to shift the focus from variable importance measures of factor levels to factors themselves and to develop variable importance measures for factors included in the model. Therefore, this work is exclusively for modeling with categorical variables as predictors. This work contributes to the further development of GLM modeling to make it even more practical due to this added value. This study also aims to provide benchmark estimates to allow for the regulation of insurance rates using GLM from the variable importance aspect.

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Towards Explainability of Machine Learning Models in Insurance Pricing

Cited by 3 publications

References 20 publications

Machine Learning in Forecasting Motor Insurance Claims

Machine Learning in Forecasting Motor Insurance Claims

Explainable Artificial Intelligence (XAI) in Insurance

Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions

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