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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

保险治理与公司违规——董事高管责任保险的治理效应研究

公司治理机制被认为是影响公司违规的重要因素,然而董事高管责任保险作为一种重要的外部治理机制,是否会影响公司违规尚未得到充分研究。本文研究发现董事高管责任保险显著降低公司违规概率,符合监督效应假说。经工具变量法、Heckman两阶段模型和倾向得分匹配法稳健性检验,上述结论依然成立。影响机制分析表明,董事高管责任保险显著降低了公司违规倾向,显著增加了违规后被稽查的概率,并降低了上市公司的第一类代理成本。对董事高管责任保险的监督职能做进一步分析发现:(1)董事高管责任保险对上市公司经营违规和领导人违规的监督效应更为显著,但对信息披露违规的治理作用并不显著;(2)董事高管责任保险发挥的监督职能与股权属性和保险机构股东治理存在替代效应,与外部审计师治理和董事长CEO二职分离存在互补效应;(3)分组检验结果表明,董事高管责任保险对公司违规的监督效应在外部监管环境较差或者公司内部信息透明度较高的情况下更加显著。本文既提供了保险合约通过公司治理渠道影响公司违规的证据,同时也表明保险机构通过董事高管责任保险为中国资本市场提供了一种较为有效的公司外部治理机制。     

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.     

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.     

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.     

Fraud Detection Using a Multinomial Logit Model With Missing Information

Recently, Artís, Ayuso, and Guillén (2002, Journal of Risk and Insurance 69: 325–340; henceforth AAG) estimate a logit model using claims data. Some of the claims are categorized as “honest” and other claims are known to be fraudulent. Using the approach of Hausman, Abrevaya, and Scott‐Morton (1998 Journal of Econometrics 87: 239‐269), AAG estimate a modified logit model allowing for the possibility that some claims classified as honest might actually be fraudulent. Applying this model to data on Spanish automobile insurance claims, AGG find that 5 percent of the fraudulent claims go undetected. The purpose of this article is to estimate the model of AAG using a logit model with missing information. A constrained version of this model is used to reexamine the Spanish insurance claim data. The results indicate how to identify misclassified claims. We also show how misclassified claims can be identified using the AAG approach. We show that both approaches can be used to probabilistically identify misclassified claims.     

De skandinaviske Aktuarforeningers Virksomhed i 1925

Abstract

Statistical extreme value theory provides a flexible and theoretically well motivated approach to the study of large losses in insurance. We give a brief review of the modem version of this theory and a “step by step” example of how to use it in large claims insurance. The discussion is based on a detailed investigation of a wind storm insurance problem. New results include a simulation study of estimators in the peaks over thresholds method with Generalised Pareto excesses, a discussion of Pareto and lognormal modelling and of methods to detect trends. Further results concern the use of meteorological information in the wind storm insurance and, of course, the results of the study of the wind storm claims.     

Die Rolle der Versicherungsvermittler beim Versicherungsbetrug – experimentelle Ergebnisse

The GDV estimates the amount of insurance fraud across Germany to be € 4 billion per year. This work examines, using experimental approach, the role of insurance agents in insurance fraud. This research shows that fraud behavior is not only dependent on economic motives even though these can make financial sense for the agent. Different crime detection rates and reward scenarios do not lead to any significant differences in behavior. If the insurance agent, however, completed an apprenticeship in the insurance sector, then this agent generally has a lower willingness to support the customer when committing insurance fraud. Against this, specifically men (as a research category) and holders of several policies tend to be more susceptible to support customers in insurance fraud.     

Versicherungsbetrug aus vertragstheoretischer Sicht

Insurance fraud is a significant economic problem. Following the deregulation of the German insurance market in 1994, contractual features are now possible which were previously restricted. Therefore, there is a need to analyse the insurance fraud reduction potential of this instrument. This paper investigates three forms of insurance fraud: ?Costly State Verification“, ? Costly State Falsification“ and third party fraud. Consequences for the optimal contractual design are deduced.     

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.     

Small Business Financial Distress and the “Phoenix Syndrome”—A Re‐evaluation

In a typical “phoenix syndrome” scenario, a small business entrepreneur who controls the financially distressed Company A registers Company B, to which the assets of Company A are transferred in what appears to be fraudulent conveyance. Company B serves as a vehicle through which the business is kept running, without the pressures of the business creditors. If necessary, the entrepreneur will also register Company C and repeat the process. The law usually considers the execution of a “phoenix syndrome” scheme (“phoenixizing”) to be fraud against Company A's unaware creditors. Two major problems undermine, however, the efficient regulation of “phoenix syndrome” schemes. First, although criminal sanctions are available, “phoenixizing” entrepreneurs are not regularly prosecuted and are usually only subject to monetary sanctions (e.g., personal civil liability to creditors). Because defrauders tend to be judgment proof, the result is sub‐optimal deterrence. Second, lawmakers have not considered a more sympathetic explanation to account for the “phoenix syndrome” phenomenon: an entrepreneur resorting to a “phoenix syndrome” scheme might actually be arranging for a last‐resort “home‐made” bankruptcy proceeding, that is, the entrepreneur might be mimicking the role of a formal bankruptcy stay on unsecured creditors' collection efforts, against the background of a cost prohibitive formal bankruptcy proceeding. Put simply, the “phoenix syndrome” scheme is, occasionally, “a poor man's” bankruptcy proceeding. Deterring a “phoenixizing” entrepreneur attempting to rescue a viable business is, of course, unwarranted, as the result is viable businesses being lost. These two problems of under‐deterrence and over‐deterrence mandate a re‐evaluation of the manner in which “phoenix syndrome” schemes are regulated. Obviously, the main question concerns implementation: How can “good” entrepreneurs, attempting to rescue a viable business, be separated from “bad” ones, who attempt to defraud or to rescue a non‐viable business? The paper discusses and evaluates several solutions. Copyright © 2012 INSOL International and John Wiley & Sons, Ltd.     

Erklärungsansätze für vertragswidriges Verhalten Versicherungsnehmern aus Sicht der ökonomischen Theorie

The two main purposes of this paper are an introduction to the economic analysis of insurance fraud and furthermore a derivation of factors that determine fraudulent behavior of policyholders on insurance markets. Consequently, we analyze the strategic decision problems of insurance companies and the policyholders and identify some factors that can help to reduce fraudulent behavior. In this context we evaluate two derived starting points for the combat against insurance fraud: fraud detection systems and a consequent charge policy of detected defrauders. We illustrate that both points can help to reduce the cost of fraud. Furthermore, we enhance our earlier analysis with respect to the empirical fact that some individuals care about fairness or — in the insurance fraud context — the legitimacy of their actions. Surprisingly, in some market situations these concerns of some policyholders do not lead to a lower fraud probability. Finally, we discuss how and to what extent insurance companies can influence such ethical concerns of policyholders. On that score, we distinguish insurance specific and insurance unspecific factors and their impact on the consumers attitudes towards insurance fraud.     

EFD: A Hybrid Knowledge/Statistical-Based System for the Detection of Fraud

Electronic Fraud Detection (EFD) assists Investigative Consultants in the Managed Care & Employee Benefits Security Unit of The Travelers Insurance Companies in the detection and preinvestigative analysis of health care provider fraud. The task EFD performs, scanning a large population of health insurance claims in search of likely fraud, has never been done manually. Furthermore, the available database has few positive examples. Thus, neither existing knowledge engineering techniques nor statistical methods are sufficient for designing the identification process. To overcome these problems, EFD uses knowledge discovery techniques on two levels. First, EFD integrates expert knowledge with statistical information assessment to identify cases of unusual provider behavior. The heart of EFD is 27 behavioral heuristics, knowledge‐based ways of viewing and measuring provider behavior. Rules operate on them to identify providers whose behavior merits a closer look by the investigative consultants. Second, machine learning is used to develop new rules and improve the identification process. Pilot operations involved analysis of nearly 22,000 providers in six metropolitan areas. The pilot is implemented in SAS Institute's SAS System, AICorp's Knowledge Base Management System, and Borland International's Turbo Prolog.