Fraud Classification Using Principal Component Analysis of RIDITs

This article introduces to the statistical and insurance literature a mathematical technique for an a priori classification of objects when no training sample exists for which the exact correct group membership is known. The article also provides an example of the empirical application of the methodology to fraud detection for bodily injury claims in automobile insurance. With this technique, principal component analysis of RIDIT scores (PRIDIT), an insurance fraud detector can reduce uncertainty and increase the chances of targeting the appropriate claims so that an organization will be more likely to allocate investigative resources efficiently to uncover insurance fraud. In addition, other (exogenous) empirical models can be validated relative to the PRIDIT‐derived weights for optimal ranking of fraud/nonfraud claims and/or profiling. The technique at once gives measures of the individual fraud indicator variables’ worth and a measure of individual claim file suspicion level for the entire claim file that can be used to cogently direct further fraud investigation resources. Moreover, the technique does so at a lower cost than utilizing human insurance investigators, or insurance adjusters, but with similar outcomes. More generally, this technique is applicable to other commonly encountered managerial settings in which a large number of assignment decisions are made subjectively based on ‘‘clues,‘’ which may change dramatically over time. This article explores the application of these techniques to injury insurance claims for automobile bodily injury in detail.     

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.     

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.     

Implications of IFRS for the European Insurance Industry—Insights From Capital Market Theory

The European insurance industry is currently undergoing a substantial change in financial reporting requirements. Beginning in 2005, compliance with the International Financial Reporting Standards (IFRS) has been required in the European Union. Substantial sections of the IFRS—leading to a market-oriented valuation of insurance contracts—are still under construction and will be introduced in the next few years. To date, assessment of the potential impact of the new IFRS accounting and reporting system is largely found in trade literature, and in insurance industry business leader and expert commentator statements. The tenor of opinion is that the IFRS will create a serious challenge for the European insurance industry. To evaluate the impact of IFRS more scientifically, this article applies—where indicated—capital market theory and the concept of information efficiency. The article suggests that concerns about the effects of IFRS are exaggerated, and reveals that the main area of IFRS impact on the European insurance industry is likely to be on insurance product design.     

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.     

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.     

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.     

Centralizing Insurance Fraud Investigation

The study of insurance fraud and its remedy is a hot topic of research, mainly because the problem of insurance fraud is so widespread. In the United States many state governments have setup agencies to combat fraud. These Insurance Fraud Bureaus (IFB) are typically established to gather information about potential fraudulent claims, and to advise prosecuting officers on the nature of each offense. This paper presents the conditions under which more fraud will be observed in an economy where an IFB conducts all audits than in an economy where each insurance company is responsible for its own investigation. Even if fraud increases, policyholders may be better off than in economy lacking an IFB. One unambiguous case where policyholders are always better is when the IFB conducts every investigation at a cost that is equal to the industry's average.     

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.     

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).     

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.     

Versicherungsbetrug als ökonomisches Problem: Eine vertragstheoretische Analyse

Fraudulent claims are a well-known phenomenon and a serious problem in some insurance markets. The purpose of this paper is twofold. First of all, it explains why insurance fraud can be considered as a contractual problem. Consequently, selected instruments to fight insurance fraud and problems concerning the punishment of detected defrauders are analyzed by using existing results of contract theory.     

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.     

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.     

IFRS 4 Insurance Contracts — verbesserte Versicherungsrechnungslegung?

IFRS 4, issued at March 31^st, 2004, represents phase I of the IASB project on insurance accounting enabling insurance companies in the European Economic Area to prepare their financial statements according to IAS/IFRS by providing basic rules. The incentives resulting from the ?temporary“ declaration are rather complex; IFRS 4 contains only few provisions including temporary choices in accounting treatment. Although phase II of the insurance project dealing with insurance liabilities and other actuarial positions is rather well predictable in many respects the IASB keeps a significant scope of discretion for this phase. IFRS 4 does not represent a self-contained accounting concept; the asset and the debit side of the balance sheet are not coordinated, the former is characterized by a significantly higher volatility. IFRS 4 merely aims at ?limited improvements to accounting (…) for insurance contracts“. This moderate objective can be regarded as accomplished. Some of the new mandatory regulations, however, are internationally controversial issues. The effects of IFRS 4 on the price of shares and the cost of capital of insurance companies represent a worthwhile issue for research. Increased transparency, especially due to additional disclosure requirements, and enhanced volatility are opposing each other, and it is rather difficult to assess which effect will predominate under which circumstances.     

现阶段商业银行汽车贷款保证保险问题分析--兼评保监会《关于规范汽车消费贷款保证保险业务有关问题的通知》

保监会《关于规范汽车消费贷款保证保险业务有关问题的通知》在贷款流程、除外责任、索赔顺序、承保期限等方面对银行汽车贷款产生了较大的影响。该《通知》将被保险人(银行)的实质性审查义务作为保证保险合同生效的要件值得商榷,也没有合理、公平地划分保险公司和银行之间的风险。对此,作者认为,商业银行在汽车消费贷款中不能过分依赖保证保险,积极探索汽车贷款风险防范的其他途径,巧妙利用保险公司的先索抗辩权并不包括汽车的其他财产险、偷盗险等险种的有利条件,注意区分汽车贷款诈骗的情况,严防保险公司对诈骗做扩大解释,从而有效防范和化解汽车贷款义务中存在的风险。     

Selection Bias and Auditing Policies for Insurance Claims

Selection bias results from a discrepancy between the range of estimation of a statistical model and its range of application. This is the case for fraud risk models, which are estimated on audited claims but applied on incoming claims in the design of auditing strategies. Now audited claims are a minority within the parent sample since they are chosen after a severe selection performed by claims adjusters. This article presents a statistical approach that counteracts selection bias without using a random auditing strategy. A two‐equation model on audit and fraud (a bivariate probit model with censoring) is estimated on a sample of claims where the experts are left free to take the audit decision. The expected overestimation of fraud risk derived from a single‐equation model is corrected. Results are close to those obtained with a random auditing strategy, at the expense of some instability with respect to the regression components set. Then we compare auditing policies derived from the different approaches.     

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.     

The adoption of International Financial Reporting Standards in a non-colonized developing country: The case of Nepal

The purpose of this paper is to provide a systematic and rigorous analysis of the accounting environment in Nepal. Based on the accounting ecology framework developed by Gernon and Wallace (1995) and interviewing selected key stakeholders, it critically examines issues related to the adoption of International Financial Reporting Standards (IFRS) in Nepal. It contributes to the literature by examining issues associated with the adoption of IFRS in a non-colonized developing country. This study finds that the decision to adopt IFRS in Nepal is not driven by the needs of local organizations and is rather imposed by donor organizations such as the Asian Development Bank, International Monetary Fund and World Bank. The findings of this study provide evidence that the adoption of IFRS is likely to be problematic due to the country's contextual environment. Specifically, there is a severe lack of qualified accountants in Nepal and the accounting profession is not ready to adopt IFRS. The study also finds that social problems such as widespread corruption and fraud are likely to cause problems for the adoption of IFRS.     

Prosecuting Insurance Fraud—A Case Study of the Massachusetts Experience in the 1990s

There is widespread agreement that insurance fraud is a major problem in the United States. There is little agreement, however, in what constitutes insurance fraud in the many articles and research papers published on the subject during the past ten years. The term ‘‘fraud’’ carries the connotation that the activity is illegal and, hence, that prosecution and conviction are potential outcomes of a specific fraud. Accepting that premise allows us to adopt the legal definition of fraud in the insurance context and to examine the experience of dealing with insurance fraud in terms of property‐liability insurance lines. Specifically, we examine ten years of data on referrals and disposals of incidents of suspected fraud as processed by the Insurance Fraud Bureau of Massachusetts to provide estimates of the distribution of types of people who perpetrate a variety of insurance frauds. We compile conviction rates, sentencing outcomes, and recidivism rates in detail to illuminate the law enforcement process and to gauge the deterrent effect of prosecuting insurance fraud in the criminal courts. The Massachusetts data lead us to conclude that the number of cases of convictable fraud is much smaller than the prevailing view of the extent of fraud; that the majority of guilty subjects have prior (noninsurance) criminal records; and that sentencing of subjects guilty of insurance fraud appears effective as both a general and specific deterrent for insurance fraud but ineffective as a specific deterrent for other crime types, as the recidivism rate appears no different from the general property criminal's recidivism rate.