Articles - Do insurers adjust prices for adoption of loss prevention


In theory, loss prevention should, as the name implies, reduce the loss for the risk carriers and the expected present value of losses. In a competitive insurance market, where the premium is given through market bidding, expected potential savings from investing in loss prevention technologies should become measurable in the insurance price. Based on a new PhD study, this article conveys the main results of the question: What influence has the use of property loss prevention technology on property insurance pricing?

 By Simon Sølvsten, Head of Organizational Resilience Research at WTW

 This understanding is sought empirically by regressing price of policy on number of bidding carriers, total insured square metres, history of experienced claim events, all claims cost and loss prevention technologies in use.

 Main results
 There is a tendency in the data that shows that smaller claims influence the cost of insurance more than larger claims when plotting the data. This aligns with the pure premium method which states claims influence the cost of insurance. It is therefore expected that claims likely matter when underwriters set the price of the contract. As large claims and frequency claims potentially have different impacts on the insurance premium, this relationship was further investigated. It was found that the size of claims has a significant yet different influence on prices. The results of the empirical analysis show that as the size of the claim increases, the relative influence the claim has on the price decreases.

 While policyholders claim history ex-ante risk transfer significantly influences the cost of insurance, little evidence supports that the use of loss prevention technologies influences the price. Only water leak detection technology seems to have any measurable downward influence on the price at the .9 confidence level. This leads us to the understanding that technologies that reduces the cost in the tail of the risk distribution where the probability for claims are lowest have less influence on the price compared to technologies that limit smaller yet likely more often experienced damages. While risk carriers’ price should reflect policyholders’ risk, risk heterogeneity is likely more challenging to measure in the tail of the distribution where there are few claims. It is therefore likely that the influence of loss prevention technologies is muted if they primarily influence severe and costly damages.

 Findings
 The policyholders claim history ex-ante risk transfer significantly influences the cost of insurance; however, little evidence supports that the use of loss prevention technologies influences the price.

 It was found that the size of claims has a significant yet different influence on prices. The results of the empirical analysis show that as the size of the claim increases, the relative influence the claim has on the price decreases.

 There is only limited support found in the empirical analysis to back investment in loss prevention, the policyholder may be best served by determining investment strategies for loss prevention technology in order to minimize own direct costs rather than for the lowering of insurance prices.

 Conclusion
 As only limited support is found in the empirical analysis to back investment in loss prevention, the policyholder may be best served by determining investment strategies for loss prevention technology in order to minimise own direct costs rather than for the lowering of insurance prices. One explanation may be that all municipalities have invested to a degree where further investments no longer influence the price of the contract. This explanation seems unlikely due to the variation in the use of loss prevention technologies amongst the municipalities. Another explanation may be that there is not sufficient competition between insurers for the price to reflect the marginal cost in the contract. The latter is consistent with the findings in the empirical analysis that highlight an increased number of competitors significantly reduce the price.

 Data
 The analysis benefits from a comprehensive dataset composed in collaboration with the industry. The dataset was collected from a total of 225 insurance bids from 72 insurance contracts. Each contract consists of grouped buildings portfolios with more than 12 thousand building addresses, 19 million square metres and 364 billion Danish kroner in property value. The contracts cover 40 different municipalities from 2008 to 2018. The data consist of detailed information on the insured building's characteristics, claims history, insurance coverage and bids from winning and losing tenders

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