Recentering politics in risk governance: auto insurance negotiations in 1990s Baltimore

A Proposal for Change: Affordable Automobile Insurance ABSTRACT The insurance industry is failing to satisfy its urban auto insurance customers' affordability needs. Ths has contributed to a serious industry image problem. The public is demanding that the industry find a solution. A National Coalition for Auto Safety is proposed involving insurers, consumer groups, and safety interest organizations. Operating at both the national and state levels, the Coalition would mount a comprehensive attack on auto accident costs encompassing auto damageability factors, occupant protection measures, driver behavior modification, traffic law enforcement, and various other elements of our urban traffic systems. The primary purpose of any business must be to identify and satisfy the needs of its customers. That is its reason for being, and if it can not perform satisfactorily, then it will not survive long in the marketplacE. In recent months, in too many places and for too many people, our industry has been failing to satisfy its customers, not because it does not have hard-working people, good systems, or sufficient capital, but because it has not been sensitive enough to the need for affordable insurance. The result has been a weakening of our image and credibility with the public. This conclusion is well documented in the results of a recent survey by the Opinion Research Corporation. It showed that the public put the property-liability insurance business third from the bottom when asked to rank the image of 24 major industries. Only the tobacco and nuclear power industries got lower ratings. Suits filed against several major insurers and ISO by the attorneys-general of some 19 states alleging a massive insurance industry conspiracy certainly have not helped, though many people have no clear notion of what the charges actually involve. On another front, the passage and subsequent judicial approval of most provisions of Proposition 103 in California have not only created a major threat for us in our largest and fastest growing market, but are stimulating consumer activists and opportunistic politicians to attempt similar actions in at least 20 other states. These happenings are further reinforcing our reputation as an industry that has poor relations with the public. In recent years, our image has also been tarnished by unacceptable volatility in the commercial lines market, a situation aggravated by wide interest rate fluctuations and high liability losses caused, in part, by a changing tort liability system. The result has been a flight by some of our commercial lines customers from traditional insurance to other methods of handling risk. Some observers feel we will never get many of these customers back. The major problem for insurers at the present time is unquestionably the spreading public discontent with auto insurance rates. One can begin to understand why by looking at the national averages. Over the past five years, the average cost of auto insurance has risen almost 60 percent, more than three times that of the Consumer Price Index. In urban areas, this cost escalation has been even more pronounced. The end result has been quite predictable: public anger and a growing perception that insurers are somehow ripping them off. Within the insurance industry, we know only too well that escalating auto insurance costs are not the result of insurer greed, but rather of the cost of those things that insurance pays for. The cost of various medical care items are up significantly more than auto insurance rates. Competition in the crash parts business has slowed down auto repair cost increases somewhat, but even so, the latest study by the Alliance of American Insurers shows that replacement parts to rebuild a $13,000 car would cost more than $40,000--and that does not include labor. The problem is even greater in congested urban areas where auto accident claims and suits have increased significantly and where the inflationary trends in auto repair costs and medical care expenses have climbed even faster than elsewhere. …

Higher auto insurance rates in Michigan lead to a high proportion of drivers without auto insurance. Introducing options or fee schedules for personal injury protection coverage could help lead to broader, more-affordable choices.

In order to accurately determine the auto insurance rate of UBI, this paper proposes to use fuzzy controller to calculate the rate and optimize it by using the simulated annealing particle swarm algorithm with Metropolis criterion. Firstly, a fuzzy controller is constructed by selecting monthly mileage and violation times to calculate the self-underwriting coefficient. In order to eliminate the subjectivity defect of fuzzy controller, the correlation function of independent underwriting coefficient and historical risk data is proposed as the fitness function of evaluating fuzzy rules, using adaptive simulated annealing particle swarm optimization algorithm is intelligent search, according to the fitness value of continual iteration and optimize the optimal fuzzy rules. Finally, the fuzzy controller is reconstructed with the optimal fuzzy rules to estimate the auto insurance rate accurately. The results show that the adaptive simulated annealing particle swarm optimization algorithm can effectively extract the driving behavior information and can calculate the more reasonable and accurate autonomous underwriting coefficient. The results are highly correlated with the number of historical accidents and have the ability and stability of risk quantification.

Rather than regarding risk governance as a burden, science should embrace it as an opportunity to build public trust

The Relationship Between Voluntary and Involuntary Market Rates and Rate Regulation in Automobile Insurance Introduction Cross-subsidization from low-cost to high-cost consumers often has occurred in industries subject to price regulation when production costs differ across consumers.(1) The potential for increased political support from engaging in cross-subsidization may help to explain the existence of price regulation in competitively structured industries, especially when nonprice competition is likely to impede the use of price regulation to achieve profits for producers. A possible example is the competitively structured market for private passenger auto insurance, which has long been subject to a two-tiered system of rate regulation. Joskow (1973) suggested a producer protection motive for auto insurance rate regulation (also see Ippolito, 1979). While this motive probably has some historical validity, empirical evidence suggests that rate regulation on average has decreased the ratio of premiums to losses in private passenger auto insurance during the 1970s and early 1980s (Grabowski, Viscusi and Evans, 1989, Harrington, 1984a and 1987, and Pauly, Kleindorfer, and Kunreuther, 1986; also see U.S. General Accounting Office, 1986).(2) The overall market for auto insurance has two components: the voluntary market, in which firms willingly contract with consumers, and the involuntary (residual) market, in which insurers are forced to issue contracts to certain persons. Voluntary market rates are regulated in about half of the states; involuntary market rates are regulated in all states.(3) In principle, involuntary markets may be needed as a result of adverse selection, which conceivably could prevent a viable voluntary market for certain consumers. Whether unregulated markets could fail in some instances due to a lemons phenomenon that could not be overcome by price and quantity competition (e.g., Rothschild and Stiglitz, 1976) is uncertain. However, it is unlikely that adverse selection can explain the substantial size of the involuntary market in a number of states. A more likely explanation is that involuntary market rate regulation produces rate levels that essentially crowd out the voluntary market for some consumers (Mintel, 1983). Involuntary market rate regulation enables regulators to reduce rates for certain consumers. Mandatory pooling ensures that any adverse financial results are spread broadly among companies. The relationship between voluntary and involuntary market rates and rate regulation in auto insurance and, more generally, the property-liability insurance industry has received little attention in the literature. The fact that auto insurance involuntary markets in some states consistently produce substantial accounting losses is well known (see Lee, 1977, Mintel, 1983, and Grabowski, Viscusi, and Evans, 1989). Little is known about the extent to which voluntary market rates are affected by involuntary market results, or whether voluntary market rate regulation affects the relationship between involuntary market results and voluntary market premiums. Previous studies of the impact of voluntary market rate regulation have focused exclusively on the relationship between aggregate premiums and losses for the total market (i.e., for the voluntary and involuntary market combined).(4) This study analyzes the relationship between voluntary and involuntary market rates and rate regulation. A multiple regression model is estimated with cross-state data to provide evidence of the impact of voluntary market rate regulation and a measure of involuntary market deficits (defined below) on the overall ratio of premiums to losses in the private passenger auto insurance market from 1979 through 1981. As in previous work, the results suggest that voluntary market rate regulation reduced the ratio of premiums to losses for the overall market. They also suggest that voluntary market rate regulation reduced voluntary market premiums in states with small involuntary market deficits. …

Pricing using a Generalised Linear Model is the gold standard in the auto insurance industry and rate regulation. Generalised Additive Model applications in insurance pricing are receiving increasing attention from academic researchers and actuarial pricing professionals. The actuarial practice has constantly shown evidence of significantly different premium rates among the different rating territories. In this work, we build predictive models for claim frequency and severity using the synthetic Usage Based Insurance (UBI) dataset variables. First, we conduct territorial clustering based on each location’s claim counts and amounts by grouping those locations into a smaller set, defined as a cluster for rating purposes. After clustering, we incorporate these clusters into our predictive model to determine the risk relativity for each factor level. Through predictive modelling, we have successfully identified key factors that may be helpful for the rate regulation of UBI. Our work aims to fill the gap between individual-level pricing and rate regulation using the UBI database and provides insights on consistency in using traditional rating variables for UBI pricing. Our main contribution is to outline how GAM can address a more complicated functionality of risk factors and the interactions among them. We also contribute to demonstrating the territory clustering problem in UBI to construct the rating territories for pricing and rate regulation. We find that relativity for high annual mileage driven is almost three times that associated with low annual mileage level, which implies its importance in premium calculation. Overall, we provide insights into how UBI can be regulated through traditional pricing factors, additional factors from UBI datasets and rating territories derived from basic rating units and the driver’s location.

This article examines the effect of price, income, and perceived risk on the demand for three major automobile insurance coverages: bodily injury, comprehensive, and collision. The research approach involved a cross-sectional analysis of insurance consumption patterns in the 359 towns and cities in the state of Massachusetts in 1979. The major findings were that the demand for the three coverages was generally inelastic with respect to price and income, that the demand for comprehensive and collision coverages was price-elastic at prices 1.6 times the state average price, and that the demand for comprehensive and collision coverages increased substantially from areas of low density to areas of moderate density. In the absence of other means of transportation, the automobile is a virtual necessity for many people. Given the strong dependency on cars and the substantial risks associated with owning and operating one, the purchase of automobile insurance is an important consumer decision, the significance of which is amplified by the high cost of insurance. In terms of single expenditures, automobile insurance ranks high among consumer goods and services, and overall it absorbs nearly 2 percent of consumers' disposable incomes [ I]. The high cost of automobile insurance, combined with the recent sharp rise in rates, has generated considerable controversy over the regulation of automobile insurance. Of particular significance are the current theoretical arguments advocating alternative methods for setting overall insurance rates and the rates for particular classes of drivers [5, 10, 14, 15, 17]. Fundamental to these arguments are key assumptions regarding consumers' utility and demand for automobile insurance. But despite considerable research on the demand for a wide variety of goods and services, surprisingly little research has been published on the determinants of the demand for automobile insurance. This article attempts to bridge this gap in insurance research by assessing some of the major factors affecting the demand for automobile insurance

The use of context-sensitive insurance telematics data in auto insurance rate making

The goal of this article is to test whether the threat of regulating (or of more stringent regulation of) automobile liability insurance as portrayed in the popular and industry press induces insurers to change the way they price their policies. More to the point, using quarterly state data from 1984 to 1993, the author attempts to determine whether insurance companies reduced premium increases to avoid regulation, a test the article will call the Regulatory Threat Hypothesis. The results suggest that automobile liability insurance premiums increased at a slower pace (or decrease) in the presence of a regulatory threat. INTRODUCTION AND MOTIVATION It is well known that firms react to outside pressure. Many companies have public relations departments to deal with pressure groups and other outside forces that may affect profits. Insurance companies faced such outside pressure in the mid- to late-1980s during the so-called insurance liability crisis. This crisis affected all types of liability insurance, including personal automobile liability insurance. [1] The 1980s was also a period of great political pressure on state regulators. Consumer groups throughout the United States petitioned state regulators to mandate insurance firms to reduce premiums, or at least the rate of increase. One consumer group collected so many signatures that California held a referendum in November 1988 on automobile insurance premiums regulation. The referendum was known as Proposition 103. [2] The referendum basically asked whether insurance companies should be mandated to reduce automobile liability premiums and whether any premium increase should be approved by an elected insurance commissioner. [3] The popular vote was almost evenly divided, but ultimately Proposition 103 passed with 51 percent of the vote. A main driver of the vote was the behavior of city dwellers (especially in Orange County) who saw an opportunity to extract money from suburban residents. Higher premiums are paid in cities, and city dwellers voted in favor of Proposition 103 since it asked for rates to be based on experience rather than geographic location. This behavior would follow the argument initiated by Peltzman (1976), who argued that different groups use their political clout to influence regulation. The vote rocked the stock market, as the value of insurance companies publicly traded plummeted. Fields et al. (1990) found that insurance companies doing business in California had an average cumulative abnormal return of -6.9 percent, which means that insurers' stock prices under-performed the market by 6.9 percent. In addition, the more business a company had in California, the greater the negative cumulative abnormal return. What is even more surprising is that a firm's proportion of business in states neighboring California also had a negative effect on the cumulative abnormal return. Moreover, the stock price of some firms with no operation in California also fell. [4] One possible explanation for this phenomenon is that investors in firms operating in states neighboring California were afraid that insurance rates were going to be controlled there as well. In fact, this concern may have been well founded; according to a survey, 90 percent of Americans would be in favor of passing a law similar to California's Proposition 103. [5] If investors perceived threats of regulation in states other than California, then one has to wonder whether the insurance companies themselves perceived such regulatory threats. If the insurance industry acknowledges the possibility of regulation, then it seems natural to conclude that it will do something to reduce the probability of such regulation. The question is, What should the industry do? The insurance industry can react to the threat of regulation in at least two ways. The first is to influence the regulator so that it becomes more conciliatory toward insurers. …

Feature extraction of auto insurance size of loss data using functional principal component analysis

이 연구는 교통사고감소를 위한 자동차보험의 지역요인 반영에 대해 다루고 있다. 연구의 목적은 지역별 자동차보험 적용방안에 대한 과학적인 검증절차를 수립하는데 있다. 이를 위해 이 연구는 지역별로 서로 다른 교통환경 요인에 대한 자동차보험 손해율과의 상관분석을 수행하였으며, 그 타당성을 검증하였다. 또한, 교통사고의 주된 원인인 인적요인에 해당하는 교통문화를 지역별로 구분하여 손해율간의 상관관계와 모형도를 제시하였다. 이러한 결과를 토대로 지역별로 교통사고를 유발하는 다양한 교통환경이 있음에도 불구하고 자동차보험요율은 획일적으로 적용되고 있는 문제점을 제기하였으며, 이를 해결하기 위한 방안으로 지역요인을 반영한 자동차보험정책의 도입을 주장하였다. This study dealt with regional characteristics in car insurance for reduction of traffic accidents. The objective was to establish scientifically the verification procedure in the application of regional auto insurance rate. To specify the objective, this study conducts the correlation analysis between factors which are various in each local traffic environment and the loss ratio in automobile insurance. Also, this provides a correlation and a modal in loss ratio, classifying human factors in locality in major cause result from traffic accident. Based on the results, this brings up the problems with applying a uniform criterion for automobile insurance rate although various factors have effect on traffic accidents in locality. Therefore, what stands out most from this study is that a policy on automobile insurance applied to regional factor should be introduced.

Using a generalized linear model to determine the claim frequency of auto insurance is a key ingredient in non-life insurance research. Among auto insurance rate-making models, there are very few considering auto types. Therefore, in this paper we are proposing a model that takes auto types into account by making an innovative use of the auto burden index. Based on this model and data from a Chinese insurance company, we built a clustering model that classifies auto insurance rates into three risk levels. The claim frequency and the claim costs are fitted to select a better loss distribution. Then the Logistic Regression model is employed to fit the claim frequency, with the auto burden index considered. Three key findings can be concluded from our study. First, more than 80% of the autos with an auto burden index of 20 or higher belong to the highest risk level. Secondly, the claim frequency is better fitted using the Poisson distribution, however the claim cost is better fitted using the Gamma distribution. Lastly, based on the AIC criterion, the claim frequency is more adequately represented by models that consider the auto burden index than those do not. It is believed that insurance policy recommendations that are based on Generalized linear models (GLM) can benefit from our findings.

Insurance rating territory design and accurate estimation of territory risk relativities are fundamental aspects of auto insurance rate regulation. It is crucial to develop methodologies that can facilitate the effective design of rating territories and their risk relativities estimate, as they directly impact the rate filing and the decision support of the rate change review process. This article proposes a Gaussian Mixture Regression model clustering approach for territory design. The proposed method incorporates a linear regression model, taking spatial location as model covariates, which helps estimate the cluster mean more accurately. Also, to further enhance the estimation of territory risk relativities, we impose sparsity through sparse matrix decomposition of the membership coefficient matrix obtained from the Gaussian Mixture Regression model. By transitioning from the current hard clustering method to a soft approach, our methodology could improve the evaluation of territory risk for rate-making purposes. Moreover, using non-negative sparse matrix approximation ensures that the estimation of risk relativities for basic rating units remains smooth, effectively eliminating data noise from the territory risk relativity estimate. Overall, our novel methodology aims to significantly enhance the accuracy and reliability of risk analysis in auto insurance. Furthermore, the proposed method exhibits potential for extension to various other domains that involve spatial clustering of data, thereby broadening its applicability and expanding its usefulness beyond auto insurance rate regulation.

Predictive modeling is a key technique in auto insurance rate-making and the decision-making involved in the review of rate filings. Unlike an approach based on hypothesis testing, the results from predictive modeling not only serve as statistical evidence for decision-making, they also discover relationships between a response variable and predictors. In this work, we study the use of predictive modeling in auto insurance rate filings. This is a typical area of actuarial practice involving decision-making using industry loss data. The aim of this study was to offer some general guidelines for using predictive modeling in regulating insurance rates. Our study demonstrates that predictive modeling techniques based on generalized linear models (GLMs) are suitable in auto insurance rate filings review. The GLM relativities of major risk factors can serve as the benchmark of the same risk factors considered in auto insurance pricing.

Predictive modeling is a key technique in auto insurance rate-making and the decision-making involved in the review of rate filings. Unlike an approach based on hypothesis testing, the results from predictive modeling not only serve as statistical evidence for decision-making, they also discover relationships between a response variable and predictors. In this work, we study the use of predictive modeling in auto insurance rate filings. This is a typical area of actuarial practice involving decision-making using industry loss data. The aim of this study was to offer some general guidelines for using predictive modeling in regulating insurance rates. Our study demonstrates that predictive modeling techniques based on generalized linear models (GLMs) are suitable in auto insurance rate filings review. The GLM relativities of major risk factors can serve as the benchmark of the same risk factors considered in auto insurance pricing.