Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims

The insurance industry is concerned with the detection of fraudulent behavior. The number of automobile claims involving some kind of suspicious circumstance is high and has become a subject of major interest for companies. This article demonstrates the performance of binary choice models for fraud detection and implements models for misclassification in the response variable. A database from the Spanish insurance market that contains honest and fraudulent claims is used. The estimation of the probability of omission provides an estimate of the percentage of fraudulent claims that are not detected by the logistic regression model.     

Deterring Fraud: The Role of General Damage Awards in Automobile Insurance Settlements

Awards for pain and suffering and other noneconomic losses account for over half of all damages awarded under third‐party auto insurance bodily injury settlements. This article hypothesizes that third‐party insurers use general damage awards to reduce the incentive to submit exaggerated claims for specific damages for injuries and lost wages. Consistent with this hypothesis, the article finds evidence using data on over 17,000 closed bodily injury claims that special damage claims that exceed their expected value receive proportionally lower general damage awards than claims that do not. Among the implications of this research is the possibility that insurers will be less zealous in challenging fraudulent special damage claims under a third‐party insurance regime than they will be under a first‐party insurance regime in which access to general damages is limited.     

Detecting insurance fraud using supervised and unsupervised machine learning

Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied in this context, and there remains insufficient evidence to guide the choice between these branches of machine learning for insurance fraud detection. Accordingly, this study evaluates supervised and unsupervised learning using proprietary insurance claim data. Furthermore, we conduct a field experiment in cooperation with an insurance company to investigate the performance of each approach in terms of identifying new fraudulent claims. We derive several important findings. Unsupervised learning, especially isolation forests, can successfully detect insurance fraud. Supervised learning also performs strongly, despite few labeled fraud cases. Interestingly, unsupervised and supervised learning detect new fraudulent claims based on different input information. Therefore, for implementation, we suggest understanding supervised and unsupervised methods as complements rather than substitutes.     

Moral hazard,adverse selection,and health expenditures: A semiparametric analysis

Theoretical models predict asymmetric information in health insurance markets may generate inefficient outcomes due to adverse selection and moral hazard. However, previous empirical research has found it difficult to disentangle adverse selection from moral hazard in health care consumption. We propose a two‐step semiparametric estimation strategy to identify and estimate a canonical model of asymmetric information in health care markets. With this method, we can estimate a structural model of demand for health care. We illustrate this method using a claims‐level data set with confidential information from a large self‐insured employer. We find significant evidence of moral hazard and adverse selection.     

Moral intensity,moral awareness and ethical predispositions: The case of insurance fraud

The issue of insurance fraud by consumers continues to perplex insurance firms, costing billions of dollars per year in the United States alone. Some analysts report that 10 per cent or more of property/casualty insurance claims are fraudulent, while less than 20 per cent of fraudulent claims being detected. Consumer attitudes are becoming more tolerant of insurance fraud in recent years. Recognizing that not all insurance fraud situations are created equal, we investigate variability in perceptions of moral intensity in dissimilar insurance padding situations in a 2 (to help others versus to profit self) × 2 (a small credit union versus a large online insurer) model and compared the results between two independent samples (college students/Millennials and an older adult population). We also investigated the impact of ethical predispositions (formalism and utilitarianism) on moral awareness and moral judgment using these four scenarios. The results suggest that the Millennials may exhibit more situationalism and more lenient judgments of collaborative versus unilateral ethical violations. In particular, ‘for self’ versus ‘for others’ comparisons show striking differences between the two age groups. The results add to the growing literature in explaining intra-personal variability in moral decision making.     

Financial Intermediary Versus Production Approach to Efficiency of Marketing Distribution Systems and Organizational Structure of Insurance Companies

An examination of the efficiency of the marketing distribution channel and organizational structure for insurance companies is presented from a framework that views the insurer as a financial intermediary rather than as a “production entity” which produces “value added” through loss payments. Within this financial intermediary approach, solvency can be a primary concern for regulators of insurance companies, claims‐paying ability can be a primary concern for policyholders, and return on investment can be a primary concern for investors. These three variables (solvency, financial return, and claims‐paying ability) are considered as outputs of the insurance firm. The financial intermediary approach acknowledges that interests potentially conflict, and the strategic decision makers for the firm must balance one concern versus another when managing the insurance company. Accordingly, we investigate the efficiency of insurance companies using data envelopment analysis (DEA) having as insurer output an appropriately selected (for the firm under investigation) combination of solvency, claims‐paying ability, and return on investment as outputs. These efficiency evaluations are further examined to study stock versus mutual form of organizational structure and agency versus direct marketing arrangements, which are examined separately and in combination. Comparisons with the “value‐added” or “production” approach to insurer efficiency are presented. A new DEA approach and interpretation is also presented.     

A Multi-Line Insurance Fraud Recognition System: A Government-Led Approach in Korea

This article introduces a government-led insurance fraud detection program in Korea. The Insurance Fraud Recognition System (IFRS) uses policy and claims data from multi-lines of insurance (life, automobile, and fire), employs a three-stage statistical and link analysis to identify presumably fraudulent claims by claimant or by group, and generates system reports that the government regulator draws on to make decisions. The authors evaluate the system based on the fraud statistics and IFRS results for 2004, and offer recommendations for system improvement. This article examines existing studies about fraud, industry experiments using advanced technology, and government assistance to the insurance industry's fight against fraud in selected countries. It also provides a brief overview of the Korean insurance market, especially after the recent Asian economic crisis.     

Feasible fraud and auditing probabilities for insurance companies and policyholders

Insurance claims fraud is counted among the major concerns in the insurance industry, the reason being that excess payments due to fraudulent claims account for a large percentage of the total payments each year. We formulate optimization problems from the insurance company as well as the policyholder perspective based on a costly state verification approach. In this setting??while the policyholder observes his losses privately??the insurance company can decide to verify the truthfulness of incoming claims at some cost. We show simulation results illustrating the agreement range which is characterized by all valid fraud and auditing probability combinations both stakeholders are willing to accept. Furthermore, we present the impact of different valid probability combinations on the insurance company??s and the policyholder??s objective quantities and analyze the sensitivity of the agreement range with respect to a relevant input parameter. This contribution summarizes the major findings of a working paper written by Müller et?al. (Working Papers on Risk Management and Insurance (IVW-HSG), No. 92, 2011).     

The Cost of Pension Insurance

This article estimates the cost of the federal pension insurance program. Pension insurance claims have an important market‐risk component, which means that the cost of the exposure cannot be estimated by discounting future claims by the risk‐free rate. Moreover, owing to the complexity of the insurance contract, its price cannot be estimated with known options formulas without introducing an error of nonquantifiable magnitude. To circumvent these problems, we model the insurance program in its full complexity and use a Monte Carlo method. By hedging the exposure with a dynamic premium policy that offloads the market risk to the insureds, one can calculate the risk‐free, or actuarial, cost of that policy. One can also characterize the nature of the subsidy and its structure across insured plans. Finally, we provide an estimate of the implicit cost of the hedge function that taxpayers currently are providing for zero remuneration. The model shows that simple contingent claims models of pension insurance result in a price that is about triple the true market cost of the insurance, and that pension insurance models that ignore market risk understate the cost by half. The solution demonstrates the broad characteristics that might characterize a credible private‐sector version of pension insurance.     

Externalities From Driving Luxury Cars

Driving luxury cars creates negative externalities. Driving a luxury car increases property damage liability insurance costs for all drivers due to the striking differences in repair costs of luxury cars and nonluxury cars in Korea. In this study, we estimate the externalities related to auto accidents involving luxury cars by running a two‐part model using unbalanced individual‐level panel data on insurance claims and characteristics of the insured party. We find evidence of negative externalities in all of our results. To be specific, a 1 percent increase in luxury cars raises the property damage liability costs by 1.9–2.6 percent per claim. The estimated nationwide increase in the cost of liability due to driving of luxury cars in Korea is USD 139–196 million per year. This cost is shared by all drivers nationwide.     

Unallocated loss adjustment expense reserving

In non-life insurance, the provision for outstanding claims (the claims reserve) should include future loss adjustment expenses, i.e. administrative expenses to settle the claims, and therefore we have to estimate the expected Unallocated Loss Adjustment Expenses (ULAE) – expenses that are not attributable to individual claims, such as salaries at the claims handling department. The ULAE reserve has received little attention from European actuaries in the literature, supposedly because of the lack of detailed data for estimation and evaluation. Having good estimation procedures will, however, become even more important with the introduction of the Solvency II regulations, which require unbiased estimation of future cash flows for all expenses. We present a model for ULAE at the individual claim level that includes both fixed and variable costs. This model leads to an estimate of the ULAE reserve at the aggregate (line-of-business) level, as demonstrated in a numerical example from a Swedish non-life insurer.     

Classification techniques for the identification of falsified financial statements: a comparative analysis

In this study, I develop 10 alternative classification models using logit analysis, discriminant analysis, support vector machines, artificial neural networks, probabilistic neural networks, nearest neighbours, UTADIS and MHDIS for the detection of falsified financial statements. The models are developed using financial and nonfinancial data. The sample includes 398 financial statements, half of which were assigned a qualified audit opinion. I compare these alternatives methods using out‐of‐time and out‐of‐sample tests. The results are used to derive conclusions on the performance of the methods and to investigate the potential of developing models that will assist auditors in identifying fraudulent financial statements. Copyright © 2009 John Wiley & Sons, Ltd.     

General Insurance Deductible Ratemaking

Insurance claims have deductibles, which must be considered when pricing for insurance premium. The deductible may cause censoring and truncation to the insurance claims. However, modeling the unobserved response variable using maximum likelihood in this setting may be a challenge in practice. For this reason, a practitioner may perform a regression using the observed response, in order to calculate the deductible rates using the regression coefficients. A natural question is how well this approach performs, and how it compares to the theoretically correct approach to rating the deductibles. Also, a practitioner would be interested in a systematic review of the approaches to modeling the deductible rates. In this article, an overview of deductible ratemaking is provided, and the pros and cons of two deductible ratemaking approaches are compared: the regression approach and the maximum likelihood approach. The regression approach turns out to have an advantage in predicting aggregate claims, whereas the maximum likelihood approach has an advantage when calculating theoretically correct relativities for deductible levels beyond those observed by empirical data. For demonstration, loss models are fit to the Wisconsin Local Government Property Insurance Fund data, and examples are provided for the ratemaking of per-loss deductibles offered by the fund. The article discovers that the regression approach is actually a single-parameter approximation to the true relativity curve. A comparison of selected models from the generalized beta family discovers that the usage of long-tail severity distributions may improve the deductible rating, while advanced frequency models such as 01-inflated models may have limited advantages due to estimation issues under censoring and truncation. In addition, in this article, models for specific peril types are combined to improve the ratemaking.     

Regulation and welfare: evidence from paragraph IV generic entry in the pharmaceutical industry

This article estimates welfare effects of accelerated generic entry via Paragraph IV challenges. Using data from 2000–2008 for hypertension drugs in the United States, we estimate demand using a random‐coefficients logit model. We find consumers gain $42 billion whereas producers lose $32.5 billion from entry. This modest $9.5 billion gain in social welfare is consistent with our observation that overall consumption does not increase after entry—generic sales displace branded sales, shifting surplus downstream from producers to consumers, insurance companies, and retailers. We demonstrate significant cross‐molecular substitution and discuss challenges in determining what fraction of downstream surplus actually goes to consumers.     

The Impact of Insurance Fraud Detection Systems

This article analyzes the impact of detection systems in an insurance fraud context. In a noncommitment Costly State Verification setting insurers can only detect fraudulent claims by performing costly audits, and policyholders are overcompensated by the optimal insurance contract. We show that auditing becomes more effective and overcompensation can be reduced, when insurers are able to condition their audits on the information provided by detection systems.     

Number of Accidents or Number of Claims? An Approach with Zero‐Inflated Poisson Models for Panel Data

The  hunger for bonus  is a well-known phenomenon in insurance, meaning that the insured does not report all of his accidents to save bonus on his next year's premium. In this article, we assume that the number of accidents is based on a Poisson distribution but that the number of claims is generated by censorship of this Poisson distribution. Then, we present new models for panel count data based on the zero-inflated Poisson distribution. From the claims distributions, we propose an approximation of the accident distribution, which can provide insight into the behavior of insureds. A numerical illustration based on the reported claims of a Spanish insurance company is included to support this discussion.     

E. J. Moorhead (1910 – 2004)

Abstract

As is well known in actuarial practice, excess claims (outliers) have a disturbing effect on the ratemaking process. To obtain better estimators of premiums, which are based on credibility theory, Künsch and Gisler and Reinhard suggested using robust methods. The estimators proposed by these authors are indeed resistant to outliers and serve as an excellent example of how useful robust models can be for insurance pricing. In this article we further refine these procedures by reducing the degree of heuristic arguments they involve. Specifically we develop a class of robust estimators for the credibility premium when claims are approximately gamma-distributed and thoroughly study their robustness-efficiency trade-offs in large and small samples. Under specific datagenerating scenarios, this approach yields quantitative indices of estimators’ strength and weakness, and it allows the actuary (who is typically equipped with information beyond the statistical model) to choose a procedure from a full menu of possibilities. Practical performance of our methods is illustrated under several simulated scenarios and by employing expert judgment.     

Insurance Fraud

Insurance fraud is a major problem in the United States at the beginning of the 21st century. It has no doubt existed wherever insurance policies are written, taking different forms to suit the economic time and coverage available. From the advent of “railway spine” in the 19th century to “trip and falls” and “whiplash” in the 20th century, individuals and groups have always been willing and able to file bogus claims. The term fraud carries the connotation that the activity is illegal with prosecution and sanctions as the threatened outcomes. The reality of current discourse is a much more expanded notion of fraud that covers many unnecessary, unwanted, and opportunistic manipulations of the system that fall short of criminal behavior. Those may be better suited to civil adjudicators or legislative reformers. This survey describes the range of these moral hazards arising from asymmetric information, especially in claiming behavior, and the steps taken to model the process and enhance detection and deterrence of fraud in its widest sense. The fundamental problem for insurers coping with both fraud and systemic abuse is to devise a mechanism that efficiently sorts claims into categories that require the acquisition of additional information at a cost. The five articles published in this issue of the Journal of Risk and Insurance advance our knowledge on several fronts. Measurement, detection, and deterrence of fraud are advanced through statistical models, intelligent technologies are applied to informative databases to provide for efficient claim sorts, and strategic analysis is applied to property‐liability and health insurance situations.     

The Economic Effects of Road Safety Improvements: An Insurance Claims Analysis

This article demonstrates the feasibility of exploiting insurance claims data to estimate the marginal benefits to society of highway infrastructure improvements. We construct a unique database linking claims expenditures for a major auto insurer in Michigan to infrastructure investments at 62 intersections in the City of Detroit, and conclude that the addition of a left‐turn lane, or left‐turn phase in the signal, decreases the insurer's average monthly claims costs at a representative intersection by $944 or $1,062, respectively. The evidence also indicates that these cost savings are a result of reductions in accident severity, rather than being a consequence of fewer accidents.     

Market Pricing of Deposit Insurance

We provide an approach to the market valuation of deposit insurance that is based on reduced-form methods for the pricing of fixed-income securities under default risk. By reference to bank debt prices as well as qualitative-response models of the probability of bank failure, we suggest how a risk-neutral valuation model for deposit insurance can be applied both to the calculation of fair-market deposit insurance premia and to the valuation of long-term claims against the insurer.