A Robust Unsupervised Method for Fraud Rate Estimation

If one is interested in managing fraud, one must measure the fraud rate to be able to assess the degree of the problem and the effectiveness of the fraud management technique. This article offers a robust new method for estimating fraud rate, PRIDIT‐FRE (PRIDIT‐based Fraud Rate Estimation), developed based on PRIDIT, an unsupervised fraud detection method to assess individual claim fraud suspiciousness. PRIDIT‐FRE presents the first nonparametric unsupervised estimator of the actual rate of fraud in a population of claims, robust to the bias contained in an audited sample (arising from the quality or individual hubris of an auditor or investigator, or the natural data‐gathering process through claims adjusting). PRIDIT‐FRE exploits the internal consistency of fraud predictors and makes use of a small audited sample or an unaudited sample only. Using two insurance fraud data sets with different characteristics, we illustrate the effectiveness of PRIDIT‐FRE and examine its robustness in varying scenarios.     

Assessing Consumer Fraud Risk in Insurance Claims

Abstract

We present an unsupervised learning method for classifying consumer insurance claims according to their suspiciousness of fraud versus nonfraud. The predictor variables contained within a claim file that are used in this analysis can be binary, ordinal categorical, or continuous variates. They are constructed such that the ordinal position of the response to the predictor variable bears a monotonic relationship with the fraud suspicion of the claim. Thus, although no individual variable is of itself assumed to be determinative of fraud, each of the individual variables gives a “hint” or indication as to the suspiciousness of fraud for the overall claim file. The presented method statistically concatenates the totality of these “hints” to make an overall assessment of the ranking of fraud risk for the claim files without using any a priori fraud-classified or -labeled subset of data. We first present a scoring method for the predictor variables that puts all the variables (whether binary “red flag indicators,” ordinal categorical variables with different categories of possible response values, or continuous variables) onto a common –1 to 1 scale for comparison and further use. This allows us to aggregate variables with disparate numbers of potential values. We next show how to concatenate the individual variables and obtain a measure of variable worth for fraud detection, and then how to obtain an overall holistic claim file suspicion value capable of being used to rank the claim files for determining which claims to pay and the order in which to investigate claims further for fraud. The proposed method provides three useful outputs not usually available with other unsupervised methods: (1) an ordinal measure of overall claim file fraud suspicion level, (2) a measure of the importance of each individual predictor variable in determining the overall suspicion levels of claims, and (3) a classification function capable of being applied to existing claims as well as new incoming claims. The overall claim file score is also available to be correlated with exogenous variables such as claimant demographics or highvolume physician or lawyer involvement. We illustrate that the incorporation of continuous variables in their continuous form helps classification and that the method has internal and external validity via empirical analysis of real data sets. A detailed application to automobile bodily injury fraud detection is presented.     

Longitudinal modeling of insurance claim counts using jitters

Modeling insurance claim counts is a critical component in the ratemaking process for property and casualty insurance. This article explores the usefulness of copulas to model the number of insurance claims for an individual policyholder within a longitudinal context. To address the limitations of copulas commonly attributed to multivariate discrete data, we adopt a ‘jittering’ method to the claim counts which has the effect of continuitizing the data. Elliptical copulas are proposed to accommodate the intertemporal nature of the ‘jittered’ claim counts and the unobservable subject-specific heterogeneity on the frequency of claims. Observable subject-specific effects are accounted in the model by using available covariate information through a regression model. The predictive distribution together with the corresponding credibility of claim frequency can be derived from the model for ratemaking and risk classification purposes. For empirical illustration, we analyze an unbalanced longitudinal dataset of claim counts observed from a portfolio of automobile insurance policies of a general insurer in Singapore. We further establish the validity of the calibrated copula model, and demonstrate that the copula with ‘jittering’ method outperforms standard count regression models.     

Claims Auditing in Automobile Insurance: Fraud Detection and Deterrence Objectives

Research on insurer management of opportunism in claiming has developed in two parallel literatures. One is a theoretical literature on insurance contracting that yields predictions about the nature of optimal auditing strategies for the deterrence of fraud. The other is a literature based upon statistical analysis of claims that yields empirical strategies for the detection of fraudulent claims. This article links the two literatures by providing an empirical assessment of insurers’ auditing practices in relation to theoretical predictions. The analysis makes use of a data set on the disposition of more than 1,000 randomly selected automobile personal injury protection claims settled in the state of Massachusetts. The findings of the article are consistent with the use of rational auditing strategies by insurers and with the use of audits for both deterrence and detection.     

Litigation Patterns in Automobile Bodily Injury Claims 1977–1997: Effects of Time and Tort Reforms

This study uses data from the Insurance Research Council to investigate changes in the use of attorneys and in the filing of legal claims to resolve automobile third‐party bodily injury claims between 1977 and 1997. We find results consistent with the general public perception that the use of attorneys and the filing of legal claims have increased over the study period. In addition, we find evidence that tort reforms enacted by the states have slowed the rates of increase in the use of attorneys and in the filing of legal claims to resolve automobile insurance claim disputes.     

An Empirical Analysis of the Effects of Increasing Deductibles on Moral Hazard

Using information on timing and number of claims in a unique data set pertaining to comprehensive automobile insurance with the increasing deductible provision in Taiwan, the authors provide new evidence for moral hazard. Time-varying correlations between the choice of the insurance coverage and claim occurrence are significantly positive and exhibit a smirk pattern across policy months. This empirical finding supports the existence of asymmetric information. A subsample estimation depicts insured drivers' significant responses to increasing deductibles, which implies the existence of moral hazard. According to the probit regression results, the increasing deductible makes policyholders who have ever filed claims less likely to file additional claims later in the policy year. The empirical findings strongly support the notion that the increasing deductible provision helps control moral hazard.     

Aufdeckung von Versicherungsbetrug bei Kfz-Schäden mit Hilfe des Benford-Tests

The German Insurance Association estimates a yearly amount of damage of € 1.5?bn to German motor vehicle insurance companies because of systematic fraud by insurance holders. It is supposed that about 10% of submitted claim applications contain manipulated data, therefore insurance companies are forced to complete a detailed and cost intensive case-by-case review of each single application. An alternative method to detect fraud in empiric data is the method of digital analysis based on Benford’s law. The Benford method uses a mathematical law of specific logarithmic distribution attributes of first digits. According to this approach, the data of a Benford set confirm with the expected digit distribution, if the data is not manipulated, whereas fraudulent interventions lead to a deviation from Benford’s law. Hence, until now there has not been any investigation whether the Benford method can also be applied on insurance data. The present article analyses a dataset consisting of more than 120,000 damage claim applications to answer this question as well as to identify the impact of specific characteristics on the probability of fraud contained in claim applications, such as the repair of the vehicle in a franchised or an independent workshop, the vehicle brand or the examination by insurance companies experts. Indeed it could be shown that Benford’s Law is only applicable on second digits of insurance data, but delivers very strong results here: All results of the considered characteristics could be verified by plausible arguments. For this reason insurance companies can benefit from making use of the Benford method to identify those claim applications with a high probability of fraud, which should then be reviewed in more detail so that resources can be allocated in a much more cost efficient way.     

First-Party Versus Third-Party Compensation for Automobile Accidents: Evidence From Canada

Insurance regimes for compensating losses arising from automobile accidents vary by jurisdiction, ranging from a pure tort system to a pure no-fault system, with both systems having well-documented benefits and costs. The majority of published research focuses on the benefits and costs associated with the compensation for bodily injury. This article extends the existing literature by examining the differences between first-party and third-party recovery for both physical damage and bodily injury losses in Canada. Our comparison of auto insurance costs per insured vehicle suggests that government-run, pure no-fault provinces have lower average costs than provinces with private tort and modified no-fault. Lower costs arise from the elimination of tort costs associated with noneconomic damages, lower claims settlement costs due to first-party compensation, and scales of economy arising from monopoly power. The second goal of the article is to examine the impact of first- versus third-party compensation on the settlement of property damage claims. We analyze the claim files of a large insurer that operates within both a traditional tort (third-party) environment and a first-party recovery environment for property damage. We find that in a first-party recovery regime claims are settled sooner, settlement costs are lower, and not-at-fault drivers are compensated at a higher rate than in the traditional tort environment.     

Measurement of the Severity of Opportunistic Fraud in Injury Insurance: Evidence from China

This article assesses the effects of claimant demographics and other claim characteristics on the measurement of the severity of opportunistic fraud using 96 excess claim lawsuits in personal injury insurance in China in 2000–2012. The empirical result indicates that severe opportunistic fraud that results in death is more numerous than it is for fraud that leads to disability and nondisability, which may be due to the fact that more severe injury may create greater openings for opportunistic fraud. Second, the severity of opportunistic fraud in provincial cities is lower than that in small or midsize cities because the former does not imply greater severity of opportunistic fraud. Third, the severity of opportunistic fraud in injuries from daily activity is greater than that for injuries from work and traffic accidents, implying that a higher excess claim probability and greater severity of opportunistic fraud in injuries from daily activity are consistent.     

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.     

索赔特征对保险理赔(诉讼)额影响的实证分析——基于交强险人身伤害判决数据

面对日益增多的保险理赔(诉讼),亟需加强索赔原因及索赔特征对法院判决结果的影响研究.本文通过对北京市大兴区人民法院2007年1月至2010年8月涉及人身伤害的交强险判决案例进行回归分析,发现:索赔原因与索赔人损失大小、就业状况、法律规定的赔偿上限有关;索赔人在交通事故中承担的过错责任与其性别、医疗费支出状况、是否死亡有...     

Outlier detection in healthcare fraud: A case study in the Medicaid dental domain

Health care insurance fraud is a pressing problem, causing substantial and increasing costs in medical insurance programs. Due to large amounts of claims submitted, estimated at 5 billion per day, review of individual claims or providers is a difficult task. This encourages the employment of automated pre-payment controls and better post-payment decision support tools to enable subject matter expert analysis. This paper presents how to apply unsupervised outlier techniques at post-payment stage to detect fraudulent patterns of received insurance claims. A special emphasis in this paper is put on the system architecture, the metrics designed for outlier detection and the flagging of suspicious providers which may support the fraud experts in evaluating providers and reveal fraud. The algorithms were tested on Medicaid data encompassing 650,000 health-care claims and 369 dentists of one state. Two health care fraud experts evaluated flagged cases and concluded that 12 of the top 17 providers (71%) submitted suspicious claim patterns and should be referred to officials for further investigation. The remaining 5 providers (29%) could be considered mis-classifications as their patterns could be explained by special characteristics of the provider. Selecting top flagged providers is demonstrated to be a valuable as an targeting method, and individual provider analysis revealed some cases of potential fraud. The study concludes that, through outlier detection, new patterns of potential fraud can be identified and possibly utilized in future automated detection mechanisms.     

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.     

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.     

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.     

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.     

Employer strategies to combat health care plan fraud

Each year health care fraud drains millions of dollars from employer-sponsored health plans. Historically, employers have taken a rather tolerant view of fraud. As the pressure to manage health plan costs increases, however, many employers are beginning to see the detection and prosecution of fraud as an appropriate part of a cost management program. Fraud in medical insurance covers a wide range of activities in terms of cost and sophistication--from misrepresenting information on a claim, to billing for services never rendered, to falsifying the existence of an entire medical organization. To complicate matters, fraudulent activities can emanate from many, many sources. Perpetrators can include employees, dependents or associates of employees, providers and employees of providers--virtually anyone able to make a claim against a plan. This article addresses actions that employers can take to reduce losses from fraud. The first section suggests policy statements and administrative procedures and guidelines that can be used to discourage employee fraud. Section two addresses the most prevalent form of fraud--provider fraud. To combat provider fraud, employers should set corporate guidelines and should enlist the assistance of employees in identifying fraudulent provider activities. Section three suggests ways to improve fraud detection through the claims payment system--often the first line of defense against fraud. Finally, section four discusses the possibility of civil and criminal remedies and reviews the legal theories under which an increasing number of fraud cases have been prosecuted.     

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 Incentive Effects of Increasing Per-Claim Deductible Contracts in Automobile Insurance

A new rating system of automobile insurance for vehicle damage in Taiwan was launched in 1996, introducing a deductible that increases with the number of claims. In this article, we provide a theoretical rationale for the existence of an increasing per‐claim deductible system and show that the new system is most likely an optimal choice for those insured who tend to have lower claims probability when incentives are present. Using a unique dynamic data set, we are able to conduct a natural experiment to examine the incentive effects (both positive and negative) by looking at the change in claim tendency before and after switching between two deductible plans: an increasing per‐claim deductible and a zero deductible. Our results provide direct evidence of the effects of deductible structures on claim behavior.     

Can Bonus‐Malus Allieviate Insurance Fraud?

Traditionally, insurance companies attempt to reduce (or even eliminate) fraud via audit strategies under which claims may be investigated at some cost to the insurer, with a penalty imposed upon insureds who are found to report claims fraudulently. However, it is also clear that, in a multiperiod setting, bonus‐malus contracts (increases in subsequent premiums whenever a claim is presented) also provide an incentive against fraud. In this article, we consider a model in which, conditional upon the client renewing his contract, the only mechanism used to combat fraud is bonus‐malus. In this way, our model provides the opposite pole to the pure audit model. We show that in our simplified setting there exists a bonus‐malus contract that will eliminate all fraud in all periods, while guaranteeing nonnegative expected profits to the insurer and participation by the insured. We also consider the dynamics of the solution, the effect of an increase in risk aversion on the solution, and the welfare implications.