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Machine Learning in Insurance

Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand...

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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2021
Fag:
deposit insurance
implied volatility
static arbitrage
parameterization
machine learning
calibration
dichotomous response
predictive model
tree boosting
GLM
validation
generalised linear modelling
zero-inflated poisson model
telematics
benchmark
cross-validation
prediction
stock return volatility
long-term forecasts
overlapping returns
autocorrelation
chain ladder
Bornhuetter–Ferguson
maximum likelihood
exponential families
canonical parameters
prior knowledge
accelerated failure time model
chain-ladder method
local linear kernel estimation
non-life reserving
operational time
zero-inflation
overdispersion
automobile insurance
risk classification
risk selection
least-squares monte carlo method
proxy modeling
life insurance
Solvency II
claims prediction
export credit insurance
semiparametric modeling
VaR estimation
analyzing financial data
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online adgang:ONIX_20210501_9783039364473_487
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Summary:Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

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