Method and tools for insurance price and revenue optimisation

Krikler, Samuel; Dolberger, Dan; Eckel, Jacob

doi:10.1057/palgrave.fsm.4770142

Cited by 10 publications

(6 citation statements)

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“…which corresponds to the situation where the choice P * = P would produce a negative bias (under-pricing). The calculation of π * ,KL (x) assigns a higher premium to policyholders with covariates X = x such that µ(x, D) is more volatile, as can be seen in approximation (17) below. This represents policies for which lack of information on discriminatory covariates matters more, in the sense that there is a higher sensitivity to the uncertainty induced by not using the discriminatory factor D. One can thus view the bias correction in π * ,KL (x) as an implicit discriminationfree risk load.…”

Section: Attribution Of Total Portfolio Premium To Individual Policiesmentioning

confidence: 99%

“…Once the demand elasticities have been estimated, a profit maximizing pricing policy can be established in a practice referred to as price optimization, see e.g. Krikler et al [17]. Within that context, Guelman and Guillén [14] apply methods from causal inference to estimate demand elasticity functions from observational data collected by an insurer.…”

Section: Introductionmentioning

confidence: 99%

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Discrimination-Free Insurance Pricing

et al. 2020

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We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from non-protected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders' discriminatory characteristics, posing potential challenges in relation to policyholder's privacy concerns.

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Section: Attribution Of Total Portfolio Premium To Individual Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrimination-Free Insurance Pricing

et al. 2020

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“…They present a dynamic pricing model that can be applied in a manufacturing environment and led to the insight that dynamic pricing is a useful lever to reduce demand variability. Another approach indirectly related to dynamic pricing and price elasticities is undertaken by Krikler et al, who introduces the emerging field of demanddriven insurance price and revenue optimization [18].…”

Section: Introductionmentioning

confidence: 99%

Reverse pricing model in supply chains

Mujaj¹

2008

Proceedings of the 10th International Conference on Electronic Commerce

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Nowadays, reverse pricing as a special form of dynamic pricing has become an increasing concern in e-commerce. Reverse pricing gives potential customers an active role: The price of a transaction is not given by the supplier, but is mainly determined by the buyer's bid. This paper emphasizes the development of current reverse pricing models to price elasticities. The reason is that price elasticities, such as price elasticity of demand and supply, measure how much the quantity of demand or supply changes when its price changes. Therefore, I have investigated supply chains from a pricing perspective which yields a rich theory on coordination mechanisms for demand and supply. I have designed a new automated coordination mechanism aimed at reducing order and inventory variances. I have evaluated my proposal by conducting a simulation study using a multi-agentbased simulation system, and show that the novel approach results in a significant reduction of the order and inventory variances (socalled bullwhip effect).

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“…For example, financially sophisticated (usually richer) customers are generally perceived as more 25 In March 2011, in the case of Association Belge des Consommateurs Test-Achats and Others vs. Conseil des ministres, the European Court of Justice ruled that risk-based gender variations in insurance prices would no longer be permitted for new policies sold in the European Union after 21 December 2012. 26 In a logistic regression for renewal probability at one motor insurer, Krikler et al (2004) report a statistically significant coefficient of 1.14 for logarithm of policyholder age. Israel (2005) reports that the probability of non-renewal of a motor insurance policy at one year's and five year's duration are 10 per cent and 6.5 per cent respectively, for one insurer in Georgia, U.S.…”

Section: Special Issues In Insurance Marketsmentioning

confidence: 99%

Non-Risk Price Discrimination in Insurance: Market Outcomes and Public Policy

Thomas

2012

Geneva Pap Risk Insur Issues Pract

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Method and tools for insurance price and revenue optimisation

Cited by 10 publications

References 0 publications

Discrimination-Free Insurance Pricing

Discrimination-Free Insurance Pricing

Reverse pricing model in supply chains

Non-Risk Price Discrimination in Insurance: Market Outcomes and Public Policy

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