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Organizational Behavior and Human Decision Processes

journal homepage: www.elsevier.com/locate/obhdp

When weak sanctioning systems work: Evidence from auto insuranceindustry fraud investigationsDanielle E. Warrena,⁎, Maurice E. Schweitzerb

a Management & Global Business, Rutgers Business School-Newark & New Brunswick, 1 Washington Park, Newark, NJ 07102, United Statesb The Wharton School, University of Pennsylvania, 3730 Walnut Street, Room 544, Jon M. Huntsman Hall, Philadelphia, PA 19104-6340, United States

A R T I C L E I N F O

Keywords:Sanctioning systemsRisk perceptionEmotionFraudDeception detection

A B S T R A C T

To deter auto insurance fraud, insurance companies and law enforcement agencies investigate and prosecutesuspicious claims. We describe this sanctioning system and perceptions of this system by integrating uniquedatasets: insurance company records, interviews with insurance fraud investigators, state law enforcement data(CA, NY), and surveys of automotive insurance customers. We identify organizational constraints, such as publicrelations concerns, that limit the effectiveness of the formal sanctioning system (fewer than 1% of claims that areflagged as suspicious are ever prosecuted for fraud). We also identify psychological factors that deter consumersfrom committing fraud; consumers over-estimate the probability of detection, over-estimate the consequences ofprosecution, are sensitive to social sanctions (e.g., negative publicity), and anticipate high emotional costs, suchas shame and embarrassment, that make the prospect of committing fraud highly aversive. That is, psychologicalfactors substantially deter fraud even though the economic sanctions are weak. Our findings integrate scho-larship on sanctioning systems (Tenbrunsel & Messick, 1991) and highlight the role of organizational constraintsand psychological factors in deterring fraud.

“We are interviewing our customers. We give them every benefit of thedoubt.”

Insurance Company Special Investigator (9)

1. Introduction

Insurance fraud is a costly form of unethical behavior. Insurers faceover $80 billion of fraudulent claims each year (Coalition AgainstInsurance Fraud, 2017a), and the costs associated with these claims arepassed on to consumers. Despite special investigative units withinbusiness organizations as well as bureaus dedicated to detection, in-surance fraud remains a persistent and pervasive problem (Associationof Certified Fraud Examiners, 2017; Coalition Against Insurance Fraud,2017a).

We investigate the auto insurance fraud sanctioning system to un-derstand how organizational constraints and psychological factors in-fluence sanctioning systems in business settings. Our work builds on thefoundational research of scholars who have investigated sanctioningsystems (De Cremer & van Dijk, 2009; de Kwaadsteniet, Rijkoff, & vanDijk, 2013; McCusker & Carnevale, 1995; Mulder, Verboon, & DeCremer, 2009; Nelissen & Mulder, 2013; Verboon & van Dijke, 2011;

Tenbrunsel & Messick, 1999). In carefully designed experiments andfield surveys, these scholars identify a number of features that makesanctioning systems more effective, such as the frequency of sanctions(Tenbrunsel & Messick, 1999), the severity of sanctions (Mulder, 2009;Verboon & van Dijke, 2011), and whether sanctions are social or fi-nancial (Nelissen & Mulder, 2013). These studies provide importantinsights into specific attributes of sanctions that improve the effec-tiveness of sanctioning systems. Our investigation complements thiswork by exploring the operation of a sanctioning system in the field.

We identify a number of organizational constraints that are likely topervade many organizational settings, such as profit concerns, that limitthe effectiveness of the automotive insurance sanctioning system.However, we also identify a number of factors that make this objec-tively weak sanctioning system surprisingly effective. We find that in-dividuals do not accurately perceive the sanctioning system, and weidentify psychological factors, such as the prospect of embarrassmentand shame, that substantially deter fraud. Our findings have broadimplications for understanding how sanctioning systems work in busi-ness settings and reveal conditions that enable weak sanctioning sys-tems to work.

We investigate a particularly important sanctioning system, auto-motive insurance fraud investigations and prosecution. We report data

https://doi.org/10.1016/j.obhdp.2019.04.003Received 5 May 2018; Received in revised form 8 March 2019; Accepted 15 April 2019

⁎ Corresponding author.E-mail addresses: [email protected] (D.E. Warren), [email protected] (M.E. Schweitzer).

Organizational Behavior and Human Decision Processes xxx (xxxx) xxx–xxx

0749-5978/ © 2019 Published by Elsevier Inc.

Please cite this article as: Danielle E. Warren and Maurice E. Schweitzer, Organizational Behavior and Human Decision Processes, https://doi.org/10.1016/j.obhdp.2019.04.003

http://www.sciencedirect.com/science/journal/07495978

https://www.elsevier.com/locate/obhdp

https://doi.org/10.1016/j.obhdp.2019.04.003


mailto:[email protected]

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from multiple sources to describe the flow of claims from the initialsubmission of a suspicious claim to the prosecution and conviction offraud. To inform our investigation, we collected unique data from for-profit (e.g., an insurance company) and non-profit (e.g., an insurancebureau) organizations. In addition, we used data and features of claimswe collected from these organizations to inform survey questions thatwe asked automotive insurance customers. Through our analyses, weidentify organizational constraints that sharply limit the efficacy ofinvestigations and prosecutions, and we find a substantial disparitybetween the actual sanctioning system and subjective perceptions ofthis sanctioning system.

In Study 1, we analyzed outcomes of insurance fraud investigationsusing claim chronologies from a large U.S. based automotive insurancecompany and find that only a small percentage of suspicious claims arereferred to law enforcement (less than 4%). In Study 2, we interview 17high-performing investigators to develop a rich understanding of thedetection and referral process. Through these interviews, we identifyimportant organizational constraints that limit the effective pursuit ofauto insurance fraud. In Study 3, we analyze two samples of state-levelauto insurance law enforcement data (NY, CA) to understand the re-ferral and subsequent investigation and prosecution of auto insurancefraud by law enforcement. We find that a very small fraction of in-surance claims referred to law enforcement (less than 14%) are pur-sued, because of tight budget constraints. In Studies 4 and 5, we ex-amine consumer perceptions of auto insurance fraud prosecution andfind that (1) consumers believe auto insurance fraud is referred to lawenforcement far more frequently than our insurance company data in-dicate, (2) consumers believe referred claims are pursued by law en-forcement far more frequently than the state-level data indicate, (3)consumers believe the sanctions are more severe than our findings in-dicate, (4) consumers are concerned about emotional costs, such asshame and embarrassment, that deter fraud, and (5) consumers’ per-ceptions of shame and embarrassment are more closely aligned withtheir perceptions of the ethicality of insurance fraud than they are withtheir perceptions of the economic costs associated with insurance fraud.In Study 6, we tease apart the influence of economic and social sanc-tions by experimentally testing the effects of social and economicsanctions as deterrents and find that both social and economic sanctionsdeter insurance fraud. In sum, we find that consumers (a) misperceiveauto insurance sanctions, (b) are responsive to social sanctions, and (c)associate high emotional costs with auto insurance fraud, which aretightly tied to their perceptions that fraud is unethical.

Our investigation makes several important contributions to the or-ganizational behavior literature. First, we identify organizational con-straints and incentives, such as reputational concerns, that cause autoinsurance fraud investigators to act very differently than participantsmight in a laboratory study. Second, we document an underappreciatedlink between organizational constraints and incentives and the efficacyof the fraud sanctioning system. Third, we highlight the importance ofpsychological factors overlooked by prior work that influence thestrength of a sanctioning system. Interestingly, rather than failing todeter unethical behavior, objectively weak sanctioning systems may besurprisingly effective, because psychological factors offset the weak-nesses of the sanctioning system. Our findings underscore the sig-nificance of understanding risk perceptions, social sanctions and emo-tion in determining the efficacy of a sanctioning system and we call forfuture research to build on these findings.

1.1. Sanctioning systems

Classical economic models of deterrence presume that economicincentives guide individual behavior (Becker, 1968). For example, de-terrence theory assumes that individuals avoid wrongdoing if the costof engaging in a behavior (the likelihood that that behavior will bedetected multiplied by the magnitude of the punishment) is greaterthan the benefit of engaging in that behavior. A substantial literature

grounded in deterrence theory has advanced our understanding ofsanctioning systems in a business setting, and empirical studies havefound support for key features of this theory (Hollinger & Clark, 1982,1983; Smith-Crowe et al., 2015; Tenbrunsel & Messick, 1999;Tenbrunsel, Smith-Crowe, & Umphress, 2003).

Two primary streams of empirical research have investigatedsanctioning systems. The first stream focuses on the implementation ofa sanctioning system. Specifically, much of this work has explored theunderlying rules or principles of a sanctioning system and the will-ingness of individuals to punish or reward others (De Cremer & vanDijk, 2009; de Kwaadsteniet et al., 2013; McCusker & Carnevale, 1995).For example, this literature indicates that equality rules play an im-portant role in assigning punishments or rewards in social dilemmas (deKwaadsteniet et al., 2013).

The second stream of research focuses on the effectiveness ofsanctioning systems in deterring wrongdoing. Specifically, this workhas found that individuals are less likely to engage in a behavior whenthe penalties are more severe (Hollinger & Clark, 1983; Mulder, 2009;Verboon & van Dijke, 2011) and the sanctions are more frequent(Hollinger & Clark, 1983; Nelissen & Mulder, 2013; Tenbrunsel &Messick, 1999). Closely related work has found that informal sanctionsand social sanctions, such as expressions of disapproval or ostracism bya work colleague, are particularly effective (Hollinger & Clark, 1982;Mulder et al., 2009; Mulder et al., 2009; Nelissen & Mulder, 2013;Warren, 2019). In most cases, these investigations have, in an experi-mental setting, isolated specific attributes of a sanctioning system, suchas the severity of the sanction (for exceptions, see Hollinger & Clark,1982, 1983; Warren, 2019). A key strength of the experimental researchis that this work documents the causal relationship between factors,such as the probability of detection, and undesirable behavior. In thefield, prior deterrence research has considered employee sanctioningsystems. Specifically, Hollinger and Clark (1982) found that informalsanctioning from peers, more so than formal sanctioning from man-agement, deters employees from engaging in workplace deviance. Noprior work, however, has considered broader organizational dynamics,such as how organizational profit motives and public relations objec-tives might influence sanctioning systems in a business setting.

In our investigation, we explore the auto insurance fraud sanc-tioning system. Insurance organizations are an integral part of oureconomic system and insurance fraud harms the effectiveness of in-surance markets. Insurance fraud is both costly and insidious; fraudincreases the cost of insurance, and as premiums rise, customers be-come more likely to commit fraud. To deter fraud, economists haveargued that we need to improve the sanctioning system by boosting theprobability of detection and the magnitude of the penalty once fraud isdetected (Becker, 1968; Polinsky & Shavell, 2000). This argument,however, assumes that decision makers are well-informed, rationalactors.

Within the criminology literature, scholars have debated whether ornot individuals accurately perceive detection rates and the severity ofsanctions (see Chalfin & McCrary, 2017 for a review). For example, Leeand McCrary (2017) contrasted arrests of young offenders both beforeand after they turned 18, a discrete event that substantially increasesthe severity of criminal sanctions, because individuals over the age of18 are subject to the adulthood legal system. If individuals rationallyperceive and respond to sanctions, crime should fall immediately afterindividuals turn 18. In fact, Lee and McCrary (2017) find that crimedoes fall after individuals turn 18, but less so than expected given theincrease in sanction severity. Also, the drop in crime might even reflectan incapacitation effect, as adults are sentenced more harshly and lessable to engage in crime. In a related study, Lochner (2007) exploredhow prior arrests influence perceptions of the sanctioning system, andfound that prior exposure does improve perceptual accuracy of thesanctioning system. Though these studies offer limited insight into theunderlying psychology of sanctioning systems, they do suggest thatpotential criminals misperceive sanctioning systems.

D.E. Warren and M.E. Schweitzer Organizational Behavior and Human Decision Processes xxx (xxxx) xxx–xxx

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In experimental work, Tenbrunsel and Messick (1999) conducted aseries of laboratory studies involving cooperation dilemmas. Theyfound that weak sanctioning systems, characterized by low monitoring,were less effective at deterring defection than no sanctioning system.Importantly, participants in these studies knew both the rate of detec-tion and the potential sanctions. In practice, however, many individualslack clear information about both the rate of detection and the mag-nitude of the sanctions. We postulate that insurance customers willover-estimate the likelihood that insurance fraud is detected, referred,and prosecuted. We develop these hypotheses with respect to thegrowing literature that has identified biases, such as the availabilitybias (Slovic, 1987; Tversky & Kahneman, 1973) that distort perceptionsof risk. According to the availability bias, individuals judge the like-lihood of an event based upon the ease with which they can recall thattype of event. For example, individuals over-estimate the likelihood ofhomicides and plane crashes because these events, though quite rare inreality, are featured prominently in the news media when they occur(Johnson & Tversky, 1983). In fact, recent organizational research de-monstrates how even subtle manipulations, such as signing at the topversus bottom of the page, can influence the veracity of disclosures toinsurance companies (Shu, Mazar, Gino, Ariely, & Bazerman, 2012). Ina field study, Shu et al. (2012) find that signing at the top of the formcurtailed misrepresentation, suggesting that a subtle manipulationmight prime ethical concerns or shift perceptions of the likelihood ofdetection.

We expect perceptions of the likelihood that insurance fraud is de-tected to be characterized by the availability bias. Popular media out-lets regularly report stories about auto insurance fraud. Often, thesereports are quite memorable. For example, Donlon (2017) published amemorable article titled “Go-Pro video leads to conviction of auto in-surance fraud suspect.” In addition, government officials routinely sendpress releases of insurance fraud prosecutions (e.g., Patronis, 2018). Insome cases, government officials may even time press releases (e.g., oftax prosecutions in the weeks leading up to April 15th) to influencebehavior (Blank & Levin, 2010). News outlets are also likely to dis-proportionally report stories in which the magnitude of the perpetratedinsurance fraud is large. As a result, when individuals develop sub-jective perceptions of the rate of detection and the consequences ofbeing detected, they are likely to over-estimate the sanctions associatedwith committing insurance fraud.

H1a. Auto insurance customers perceive the probability of an insurancecompany referring a suspicious claim to law enforcement to be muchhigher than it actually is.

H1b. Auto insurance customers perceive the probability that lawenforcement prosecutes suspicious claims to be much higher than itactually is.

H2. Auto insurance customers perceive the magnitude of consequencesfor prosecuted insurance fraud to be much higher than it actually is.

1.2. Emotional costs

In addition to considering perceptions of risks and economic con-sequences, we consider the role of emotional consequences in the autoinsurance sanctioning system. Specifically, we postulate that the pro-spect of embarrassment and shame represent significant costs that deterauto insurance customers from committing fraud. Though prior workhas established that emotion is closely associated with deception (e.g.,Shalvi, Eldar, & Bereby-Meyer, 2012; Shalvi, Handgraaf, & De Dreu,2011; Gaspar & Schweitzer, 2013; Moran & Schweitzer, 2008), thiswork has primarily focused on emotion as an antecedent or a mediator(e.g., Yip & Schweitzer, 2016) rather than a cost of engaging in de-ception. Our focus on emotion fills an important gap insofar as em-pirical research of deterrence and sanctioning systems in organizationalbehavior has focused on economic consequences and ignored the

potential role of emotion as a deterrent or cost.In our investigation of the insurance fraud sanctioning system, we

consider the prospect of embarrassment and shame as potential costs.That is, we highlight the role of emotion in the insurance fraud sanc-tioning system. In general, individuals experience embarrassment whenthey fall short of others’ standards, whereas individuals experienceshame when they fail to meet their own personal standards (Smith-Crowe & Warren, 2014; Warren & Smith-Crowe, 2008). Both embar-rassment and shame are moral emotions that provide an internal signalof wrongdoing. In addition, both embarrassment and shame are highlyaversive, and the prospect of both emotions may significantly curtailinsurance fraud.

Prior work by criminologists has found that embarrassment andshame represent internal sanctions that deter wrongdoing for behaviorssuch as littering and drunk driving (Grasmick, Bursick, & Arneklev,1993; Grasmick, Bursick, & Kinsey, 1991). For example, Grasmick et al.(1993) conducted a ten-year longitudinal study of self-reported drunkdriving and found that shifts in feelings of shame, more than changes inlegal sanctions, explained the reduction in drunk driving. Althoughembarrassment and shame do not feature in traditional economicmodels of deterrence, we expect these emotions to play a critical role incurbing insurance fraud.

H3a. Customers associate auto insurance fraud with perceivedemotional costs (embarrassment and shame).

A number of scholars have found that economic sanctions may failto deter undesirable behavior. For example, Gneezy and Rustichini(2000) found that after a daycare introduced a fine for late pick-ups ofchildren, the daycare actually experienced an increase in late pick-ups.Gneezy and Rustichini (2000) argue that rather than communicatingmoral information, the fine was perceived as a price for a markettransaction. This finding is consistent with Tenbrunsel and Messick(1999) finding that economic sanctions invoked a ‘business’ rather thanan ‘ethics’ mindset. Taken together, these studies suggest that economicsanctions may not an effective method for cueing moral concerns anddeterring unethical behavior.

Reflecting upon these studies, Mulder (2009) argues that the size ofthe fine signals important information regarding the morality of thebehavior. She asserts that the economic severity of a sanction serves asa signal of immorality. More specifically, larger sanctions indicate theneed to punish with punitive damages for immoral behavior whereasweaker sanctions merely signal a need to compensate for losses and donot activate moral concern. In our work, we test this argument by ac-counting for not just the perceived economic costs, but also emotionalcosts associated with auto insurance fraud. By examining the antici-pated economic sanctions alongside anticipated shame and embar-rassment, we examine ethical judgments tied to emotion, independentof economic sanctions. Specifically, we explore how anticipated emo-tional inform perceptions of the morality of engaging in insurancefraud.

H3b. Customers’ perceptions of the morality of auto insurance fraudrelate positively to perceived emotional costs (embarrassment andshame).

1.3. Social sanctions and deterrence

We extend our conceptualization of emotional costs to the attributesof the sanctions themselves. We expect social sanctions to help explainwhy an objectively weak sanctioning system effectively deters fraud. Inan ethnography of trader deviance on financial exchanges, Warren(2019) found that social sanctions, such as ostracism and social rejec-tion from other traders, challenged, and sometimes overpowered, fi-nancial sanctions levied by the financial exchange. Building upon thisresearch, we theorize that to the extent that non-economic sanctionssuch as social sanctions cause emotional costs, we expect social


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sanctions, in addition to economic sanctions, to play a significant role indeterring auto insurance fraud. Therefore, we predict that auto in-surance fraud deterrence depends upon not only the economic sanc-tions, such as the likelihood of detection and fines, but also socialsanctions, such as how well publicized fraud cases are.

H4a. Social sanctions (e.g., publicity) deter auto insurance fraud.

H4b. Economic sanctions (e.g., fines) deter auto insurance fraud.

1.4. Research setting

Automobile insurance fraud represents one of the most expensivetypes of fraud perpetrated in the United States (Coalition AgainstInsurance Fraud, 2017a). To curtail fraud, insurance companies andgovernment agencies have developed an elaborate sanctioning system.We investigate this sanctioning system as well as perceptions of thissystem.

In Fig. 1, we detail the flow of auto insurance claims through theexisting sanctioning system. The first stage in the sanctioning systeminvolves the insurance company. In this stage, the insurance companymay flag a claim as suspicious, investigate the claim, and ultimatelyrefer the claim to law enforcement. If a claim is referred to law en-forcement, a government agency may open a file and prosecute the

claim. Each stage in this process is both time consuming and expensive,and fraud insurance investigations are characterized by a number ofpractical budgetary and impression management constraints that limitthe efficacy of this sanctioning system.

In our investigation, we integrate multiple sources of data to un-derstand this multi-organizational sanctioning system. We focus ourinvestigation on automotive insurance fraud, but our insights havebroad application for understanding organizational sanctioning sys-tems. Many organizations strive to balance competing concerns to deterwrongdoing and preserve relationships and profitability. In our setting,insurance companies investigate customers with whom they seek tomaintain positive relationships. These competing concerns significantlyinfluence how for-profit organizations investigate fraud.

In Studies 1 and 2, we describe the automotive insurance fraudsanctioning system with respect to the internal operations of a largeinsurance company. We analyze investigative chronologies of suspi-cious claims, and we report results from structured interviews withskilled investigators. In Study 3, we describe how state bureaus prose-cute fraud. By integrating organizational and state actions, we provide aunique and comprehensive understanding of the auto insurance fraudsanctioning system. From these data sources, we develop descriptivebenchmarks to understand the flow of insurance claims through thesanctioning system.

In Studies 4 and 5, we contrast consumer perceptions with the

Fig. 1. Sanctioning system for auto insurance fraud.


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reality of the sanctioning system. These studies underscore the im-portance of psychological factors, including misperceptions of risks andpsychological costs related to embarrassment and shame, that sub-stantially strengthen the auto insurance fraud sanctioning system. InStudy 6, we use an experimental design to test the causal effects ofsocial and economic sanctions on auto insurance fraud.

2. Study 1

In Study 1, we analyze a proprietary dataset of investigativechronologies of insurance fraud investigations that a large U.S. basedinsurance company shared with us. This company employs over 30,000people, including 150 special investigators who have substantial ex-perience in either law enforcement or fraud investigation. Thesechronologies range from 2 to 5 pages and provide a day-by-day de-scription of the Special Investigative Unit (SIU) investigation startingwith the initial reporting of the claim to the insurance company. Whenan insurance customer files a claim, a front-line claims agent makes aninitial decision: to process payment for the claim or to flag the claim assuspicious (Step 1 in Fig. 1). The claims in our dataset were flagged assuspicious. These claims aroused suspicion because of features of theclaim, such as when the loss happened (e.g., close to policy inception)and how the loss happened (e.g., car fire, collision with a phantomvehicle). The claims chronologies in our study reflect a random sampleof the claims that front-line agents identified as suspicious.

2.1. Data analysis

A total of 351 investigative claim chronologies served as the samplefor Study 1. The investigative chronologies of these suspicious claimsdescribe each investigative action the special investigators took to in-vestigate potential fraud and the organizational outcome of the claim.The chronology indicated if the claim had been (a) paid or (b) denied.For this study, two independent coders, blind to the purpose of thisstudy, further segmented the outcomes of the denied claims by thereferral status. More specifically, the coders coded the claims chron-ologies for whether claims were denied without referral outside theorganization or denied with referral to an outside organization (e.g.,law enforcement) for further investigation. Coder agreement was 93%.A third coder, also blind to the purpose of the study, settled disagree-ments. We created summaries of a subset of these claims (See AppendixA for one example) in Study 4 to compare these actual proportions ofdenied claims to subjective perceptions.

2.2. Results & discussion

In Fig. 2, we report summary statistics of these auto insurance claimchronologies (N = 351). Of the 351 cases, the insurance company de-nied payment to 73 claims (20.8%). We analyzed these 73 deniedclaims to calculate the likelihood that a denied claim would be referredoutside the organization for further investigation. Of these 73 claims,only 11 (15.0%) were referred for investigation outside the organiza-tion (e.g., state law enforcement, National Insurance Crime Bureau).This statistic aligns with industry standards (Association of CertifiedFraud Examiners, 2017; Lesch & Brinkmann, 2011).

In Fig. 1, we document the percentage of claims that advancethrough each stage of the auto insurance fraud sanctioning system atthe organizational level. First, we report the percentage of submittedclaims that were flagged for suspicion by front-line claims agents andreferred to the special investigative unit for investigation (1.6%). Thispercentage reflects the fraction of claims referred for investigation re-lative to the entire sample of claims submitted to the insurance com-pany.

In these data, we identify several interesting patterns. We find thatthe organizational decision to investigate a claim is rare (less than 2%of claims). In addition, the organizational decision to refer a claim

outside the organization reflects a much higher hurdle than the decisionto deny a claim. In many cases, claimants stop cooperating with theinvestigation by repeatedly failing to attend meetings or return phonecalls.

In this first stage of the sanctioning system, the insurance organi-zation makes several important decisions that reflect organizationalincentives (e.g., reputational concerns). In Study 1, we document therate at which claims are advanced through the sanctioning system. InStudy 2, we extend our investigation by interviewing special in-vestigators to gain insight into the organizational constraints and in-centives that shape the organizational sanctioning system.

3. Study 2

We conducted semi-structured telephone and in-person interviewswith 17 investigators of Special Investigative Units. The interviewsranged in duration from 60 to 90 min. Our interviews focused on ef-fective investigative practices for detecting fraud in their organizationand on constraints they faced as investigators in deterring fraud.

In our interviews, we devote specific attention to the outcomes ofthe investigations. In deciding to pay or deny a claim, insurance com-panies navigate a number of competing concerns including potentialliability and negative publicity if they deny a legitimate claim (Warren& Schweitzer, 2018). On average, special investigators require between15 and 30 days to close a claim, and investigators typically investigate1–5 claims at a time (Association of Certified Fraud Examiners, 2017).In discussing the investigative process, investigators identified severalorganizational constraints that limit their referral of suspicious claimsto law enforcement.

Of the 17 investigators we interviewed, five held managerial rolesand the remaining twelve were identified by senior management at theinsurance company as their top investigators nationwide. These top-performing investigators had worked at the insurance company be-tween six and sixteen years, and their work experience in the industryranged from six to twenty years. Prior to working in insurance fraudinvestigations, eight had worked in law enforcement.

3.1. Data analysis

We analyzed the interview data by performing open and axialcoding to understand the causes and consequences of fraud detectionprocesses in a business organization (Corbin & Strauss, 2008). Specifi-cally, we used open coding to identify key concepts in the raw data(e.g., language constraints, investigative technique, customer orienta-tion) and we used axial coding to identify relationships between theconcepts (e.g., interview structure and subsequent referrals to law en-forcement). Using axial coding, we assessed the causal and intervening

Paid Denied and not referred

Denied and referred

0

50

100

150

200

250

300

17.7%

3.1%

79.2%

Fig. 2. Study 1: status of suspicious insurance claims (N = 351).


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conditions related to the phenomenon as well as action strategies andconsequences that resulted (Corbin & Strauss, 2008). The developmentof the codes and categories was an iterative process that ultimatelyended with the identification of several key categories of organizationalconstraints. We include a sample of illustrations in Table 1. In the nextsection, we review the results of our analyses.

3.2. Results

The insurance organization has public relation motives, profit mo-tives, and legal considerations that influence the organizational sanc-tioning system. Informed by these motives and considerations, the in-surance organization constrains the actions of their investigators. Inmany cases, rather than focusing on determining whether or not fraudoccurred, investigators focus on whether or not their claimants are inbreach of their contract. For example, after investigators start an in-vestigation, many claimants stop returning phone calls and fail to show-up to meetings. Non-cooperation with an investigation is a breach ofcontract and grounds for denying a claim. In this way as well as in manyothers, investigators often prioritize preserving customer satisfactionand the organization’s reputation rather than determining whether ornot fraud has occurred.

In our interviews, we identified three interrelated, organizationalconstraints that shape the organizational sanctioning system: languageusage, investigative tactics, and customer orientation. These constraintssupport the nuanced objectives of the organization and constrainedinvestigators’ ability to obtain data, detect fraud, and subsequentlyrefer claims to law enforcement.

3.2.1. Investigative techniquesWe found that investigators cannot use aggressive interrogation

techniques that are acceptable in law enforcement. For example, withinthis organizational setting, investigators are not allowed to accuse acustomer of deception, induce fear, threaten a customer, or lie to acustomer. In fact, several investigators explained how they avoided“power” techniques that they had used in law enforcement. Thesetechniques include using seating, physical space and the presentation offacts regarding the claim. Importantly, the investigators noted the needto do your “homework” by accumulating a large set of claim facts (e.g.,database searches, canvas the scene of the loss, interview witnesses)before an interview with a claimant and then simply ask the claimant ifthey can explain inconsistencies in a non-threatening manner.

Several investigators explicitly drew sharp contrasts between thelegal and business domains by specifically detailing acts of aggressionand intimidation they had used in their law enforcement careers, butwhich are not acceptable in their current business setting. For instance,one investigator focused on approaches that emphasize physical ag-gression and intimidation, “In law enforcement, interviewers will lean in,bang fists, lie about details. At [Insurance Company], you need to be re-spectful and don’t ask questions over and over. You can’t say ‘Did you setyour car on fire?’ over and over.”

As part of the constraints on the investigative techniques, specialemphasis was given to the use of language. In the next section, wehighlight organizational restrictions that are placed on specific words inthis stage of the sanctioning system, as investigators collect informationfrom claimants.

3.2.2. Language usageThe organization guided investigators to avoid using the term

“fraud” or any related terms. That is, investigators avoided even theappropriate use of the term fraud.

The investigators detailed ways in which they worked around theorganizational boundaries. As one investigator explained, “Unlike lawenforcement, I can only use implied accusations (‘I know the truth’) withoutsaying the claimant is lying.”

Through our interviews, investigators revealed that they assidu-ously avoid using words such as “fraud” and “liar” during their inter-views. This approach reflects the organizational desire to treat clai-mants as customers and to preserve the organization’s reputation.

3.2.3. Customer orientationInvestigators also provided insight with respect to the organization’s

customer focus. In fact, reputational concerns guide investigators toconsider negative “word of mouth” and the potential for negative pub-licity from prosecuting clients. By balancing these competing concerns,investigators explicitly pursue goals that are not perfectly aligned withthe goal of detecting fraud.

Several investigators noted the importance of maintaining theircustomer base. As one investigator explained, “Insurance companies arenot in the business to fight fraud, they’re in the business to pay claims…they’re in the business to give you your money, not to get you prosecuted.”Many of the investigators viewed their organization’s approach tomanaging claim investigations as a reflection of larger insurance norms.Throughout our interviews, investigators characterized other firms as

Table 1Study 2: illustrations of organizational constraints to fraud detection.

Organizational Constraint Illustration

Language usage “You don’t ever say the f-word, ‘fraud’ or ‘liar’ in the interview. There are enormous differences between law enforcement and insurance fraud in that youadvise them of contradictions. You ask them if they can explain a situation. If they can’t, you move on. In law enforcement, you can say, ‘you’re lying to me.’”[Investigator 2]“You know they’re hiding things or being deceptive because forensics tells you that keys were used to start the car and they tell you that all of the keys areaccounted for. You can only ask, “If you’re telling me all of the keys are accounted for, how can you explain that the ignition was not defeated?” [Investigator6]“In the private sector, you can’t confront someone and say, ‘I know you’re lying, you tell me what happened.’ You cannot confront anyone and tell them they’relying.” [Investigator 9]

Investigative techniques “I just had a conversation with management about this. Because of our investigation they don’t cooperate, but we can’t classify it as ‘fraud’ even though weknow it is…A claim can have ten inconsistencies, but if the claimant walks away then the claimant is not cooperating even though we’re certain that fraud wasinvolved.” [Investigator 11]“I can’t make any threats. I can’t threaten arrest or jail. I can’t put handcuffs on the table.” [Investigator 1]“A cop can make up evidence and witnesses, they can lie to suspects, but you would be fired for deceiving interviewees here.” [Investigator 6]“We’re not police and we don’t interrogate. You need to follow the rules of the organization and laws of the organization.” [Investigator 7]

Customer orientation “You have no hammer; people rarely admit that they committed fraud. With these customer-oriented tactics, they won’t give the confession.” [Investigator 10]In the private sector, a real drawback is that you can’t get as much accomplished. We are interviewing our customers. We give them every benefit of the doubt.[Investigator 9]“The shift towards customer is an industry norm. At other companies, they refer to the ‘investigators’ as ‘claims reps’ and they can’t use the term ‘investigation.’Or, they’ll follow-up on an investigation by saying they’re assisting the claims representative in validating the claim.” [Investigator 11]“If you’re gathering information from someone and they’re a customer, you don’t want to offend them. You want to keep them as a customer. If they havecommitted fraud, I don’t want them as a customer but still need to be respectful and don’t interrogate because you don’t want them talking bad about theinsurance company to their friends.” [Investigator 7]


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taking more extreme steps to cater to customers. Importantly, the in-vestigators we interviewed viewed these customer-oriented tactics as abarrier to detecting fraud.

The organization not only operates with profit goals, but also with abroader legal and public relations framework. For example, if a con-sumer is wrongly accused, the insurance company could suffer publicoutrage and financial costs. As a result, investigators in this businesscontext prepare evidence meticulously before interviews, and exhibit apreference for merely denying payment, rather than advancing cases toprosecution.

According to the investigators we interviewed, the insurance com-pany set clear goals to support customers and avoid negative publicity,but lacked clear goals with respect to the objective of determining fraudand referring claims to law enforcement.

3.3. Discussion

Our interview data identify a structural feature that influences theeffectiveness of the insurance fraud sanctioning system. Rather thanfocusing on determining the truth, investigators are tasked with thechallenge of preserving organizational relationships and the organiza-tion’s reputation.

Investigators are also constrained by their lack of law enforcementtools. These differences influence the fraud detection process and thenature of outcomes. Ultimately, the lack of referrals to law enforcementlimits the prosecution of insurance fraud.

Investigators in business settings balance competing concerns asthey investigate deception. They assume the adversarial stance of asuspicious investigator, but at the same time seek to maintain customersatisfaction and avoid negative media attention. As a result, in-vestigators in businesses are constrained by considerations prior workhas entirely neglected. For instance, Buller and Burgoon (1996) as-sumes no constraints on the questions interviewers can use. In contrast,we found that investigators cannot use aggressive interrogation tech-niques such as repeating questions, using the term “fraud,” accusing thecustomer of deception, inducing fear, lying about evidence, or threa-tening the claimant.

By balancing competing concerns, investigators explicitly pursuegoals other than a single-minded focus on detecting deception.Interestingly, we found that these competing objectives exact a con-siderable toll on investigators. From our interviews, we found that in-vestigators were frustrated that “liars do not always see consequences.”

In the next section, we examine the law enforcement stage of thesanctioning system. By integrating organizational and state data on thesanctioning system, we gain a full understanding of the sanctioningsystem.

4. Study 3

In Study 3, we extend our investigation to examine the second stagein the insurance fraud sanctioning system: the flow of claims throughlaw enforcement. To do this, we integrate data from state bureau re-ports on insurance fraud investigations. These state reports inform ourunderstanding of the constraints government prosecutors face and thelikelihood that suspicious claims are advanced through the sanctioningsystem (i.e., the rate at which claims are investigated and prosecuted atthe state level). Although a growing literature has begun to investigateinsurance fraud from an organizational lens (e.g., Brinkmann, 2005;Lesch & Brinkmann, 2011; Warren & Schweitzer, 2018), our work is thefirst to integrate these two stages of the sanctioning system to under-stand how both organizational and state actions sanction fraud.

4.1. Data analysis

In most states, auto insurance investigations are handled by a statebureau dedicated to investigating and prosecuting insurance fraud

(Coalition Against Insurance Fraud, 2017a). In this study, we analyzethe annual reports of two large states (California and New York). Nouniform reporting guidelines exist across states, and the data we haveare rich, but limited. For example, in their state bureau reports, NewYork focuses on the type of referred claim and the opening of a lawenforcement case rather than the prosecution of the claim. That is, NewYork state distinguishes cases by the following categories: auto theft,auto fire, theft from auto, auto vandalism, auto collision damage, autofraudulent bills, auto ID cards and auto miscellaneous. In contrast,California does not provide this level of detail, but does provide sta-tistics to inform our understanding of how claims are processed andfraud suspects are convicted through the state system. California alsoidentifies the number of cases that are not processed due to a lack ofresources.

We analyze all of the state data we collected from New York(20 years of data) and California (13 years). New categories of datawere added to the reports over time, and as a result, we conducted someof our analyses on a subset of years.

4.2. Results

We first consider the ratio of the number of cases referred to thestate to the number of cases opened for investigation by the state.Opening a case represents the first step of the “government” stage of thesanctioning system (See Fig. 1 and Table 2). States generally lack theresources to investigate and prosecute every case that they receive. As aresult, state decision makers decide which cases to pursue (“open”). Tocompute the percentage of cases opened, we use data from New Yorkand California across the available years of data that we have. Wecollapse the New York data across claim type (e.g., auto theft, auto fire),and we find that the mean rate of cases opened compared to claimsreferred is 2.15%. In California, the mean rate of cases opened com-pared to claims referred was 13.2%. In Fig. 1, which illustrates the flowof cases through the deterrence system, we report California’s rate ofopened cases in 2016 (13.5%), because it offers a more conservativeview of the extent to which consumers over-estimate the likelihood ofprosecution.

We next compute the ratio of the number of opened cases to thenumber of convictions. This reflects the second step in the state sanc-tioning system. To compute this percentage, we divide the number ofcases convicted by the number of cases opened. In California we findthat the average percent of referred claims that result in a conviction is4.55% and the average percent of arrests that result in a conviction is83.8%. In Fig. 1, we only report the most recent California data

Table 2Study 3: CA referrals, cases opened, and convictions.

Year CA Referrals Received Cases opened Arrests Convictions

2016 17,955 2428 1161 10942015 20,460 2597 1135 12312014 19,578 1477 1233 8542013 17,981 2651 1343 11272012 17,259 2756 1351 10362011 16,927 2509 1241 9082010 14,894 2494 1162 9142009 14,312 2115 955 8262008 14,623 1909 748 7902007 14,357 2089 1187 8102006 14,714 1860 1066 9352005 15,378 1693 1019 8632004 15,283 1389 1287 934

Note: These data are from the Annual Report of the Commissioner published bythe California Department of Insurance. They refer to investigative activitypertaining to auto insurance claims initiated within a given calendar year, ex-cept in the case of Referrals Received which includes those received during thefiscal year (e.g. 2016 is the fiscal year 2015–2016).


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(94.2%), because New York did not provide conviction rates.Importantly, California also provides three years of data about the

number of insurance fraud cases that are not opened due to resourceconstraints. For the years 2015–2017, the percentage of cases notopened due to resource constraints ranged from 38.4 to 55.2%. Thesedata include all forms of insurance fraud cases (life insurance, workerscompensation, etc.) and are not specific to auto insurance fraud.However, these ratios inform our understanding of resource constraintsat the state level and further inform our understanding of the ways inwhich organizational constraints restrict fraud investigations.

4.3. Discussion

We find that a small percentage of claims referred to state bureausby insurance companies are opened and prosecuted. Although thisfinding is consistent with industry claims (Association of CertifiedFraud Examiners, 2017; Coalition Against Insurance Fraud, 2017a), thisrate is surprising given how selective insurance companies are in re-ferring suspicious claims to state bureaus. That is, insurance companiesare likely to pass cases to the state that are both compelling and welldocumented.

In Study 2, our insurance company investigators described businessconstraints that prevent insurance companies from fully pursuing po-tentially fraudulent claims. In this study, we find that state constraints,specifically “insufficient resources” documented in the California data,limit the sanctioning system. Quite possibly, our findings represent aconservative representation of how rare prosecution is, because NewYork and California have the highest conviction rates in the country(Coalition Against Insurance Fraud, 2017b).

Integrating our organizational data (Studies 1 and 2) and our statedata (Study 3), we identify a weak sanctioning system for auto in-surance fraud. The likelihood that a suspicious claim flagged by aclaims representative is ultimately sent to a state bureau is less than 4%.The likelihood that a claim sent to the state bureau is successfullyprosecuted (the claimant is convicted) is less than 6%. That is, in mostcases, claimants who submit suspicious claims may have their claimdenied by the insurance company, but prosecution is exceedingly rare(see Fig. 1).

Taken together, our findings reveal a weak sanctioning system forinsurance fraud. According to deterrence theory, this sanctioningsystem should fail to deter fraud. In our next two studies, we explorecustomer perceptions of this sanctioning system.

5. Study 4

In this study, we assess auto insurance customers’ beliefs about theprobability of fraud detection. Specifically, we ask customers how likelyit is that an insurance claim that was flagged for suspicion, investigated,and denied by an insurance company is referred to law enforcement.That is, we focus on perceptions of the link in the sanctioning systembetween insurance organizations and the state.

In our investigation, we focus on “soft” fraud, which we define asopportunistic fraud committed by individuals or small groups. In con-trast, “hard” fraud is committed by organized crime rings who eitherstage or fabricate losses. Soft fraud typically involves deception thateither inflates or misattributes actual losses. Soft fraud is far morecommon and though individual cases of hard fraud are quite costly, inaggregate, soft crime represents the more serious form of insurancefraud.

5.1. Data analysis

We recruited 240 auto insurance consumers (43.8% female; meanage = 37.2 years) to participate in an online survey through Amazon’sMechanical Turk in exchange for an 80-cent payment. We gave parti-cipants a summary of an actual insurance claim submitted to an auto

insurance company that was investigated for fraud (adapted from Study1). We asked participants to estimate the likelihood that the claim wasreferred to law enforcement and prosecuted. We also asked respondentsto forecast the embarrassment and shame they would feel if they hadengaged in the actions described in the scenario, to assess the moralityof the claimant’s actions, and demographic questions.

We randomly assigned participants to one of five claim summariesfrom our claims data in Study 1. In this survey, we included five dif-ferent claims that varied the type of loss. We include one of thesesummaries in Appendix A, and the remaining summaries can be foundon https://bit.ly/2C7Y6rj.

We selected claims that were flagged as suspicious, referred forinvestigation, and denied payment based upon evidence the insurancecompany investigator had gathered (e.g., video evidence that contra-vened the claimant’s statement). These claims were eligible for referralto law enforcement. To reflect industry standards and the referral rateof denied claims to law enforcement in our dataset (15.1% in Study 1),one of the five claims we selected was actually referred to law en-forcement (20%). The remaining four claims were denied payment bythe insurance company but not referred to law enforcement.

We asked respondents to read the claim summary and answer sev-eral questions.

5.1.1. Likelihood of referralFirst, we asked participants to assess the likelihood that a case like

this one would be referred to law enforcement. Specifically, we asked,“Based on what you know about insurance companies, fraud, andprosecution, what is the percent likelihood (%) that cases like this one(claims that are flagged by the insurance company as suspicious, in-vestigated by the insurance company, and denied payment) are referredto law enforcement?” We defined 0% as “Never Referred” and 100% as“Always Referred,” and we asked participants to enter numerical esti-mates.

5.1.2. Likelihood of prosecutionWe then asked participants to assess the likelihood that a case like

this one would be prosecuted. We asked, “When referred to law en-forcement, what is the percent likelihood that cases like this one (claimsthat are flagged by the insurance company as suspicious, investigatedby the insurance company, and denied payment) are prosecuted?” Asbefore, we defined 0% as “Never Prosecuted” and 100% as “AlwaysProsecuted,” and we asked participants to enter numerical estimates.

5.1.3. Emotional costsWe measured emotional costs by adapting items from Grasmick and

colleagues’ (1993) article which investigated feelings of shame andembarrassment associated with drunk driving. To adapt these measuresfor our investigation, we asked, “Consider the individual in this story. Ifyou were this individual, how much shame would you experience?”, “Ifyou were the individual in this story, how much embarrassment wouldyou feel?” and “If this happened to you, how much respect wouldfriends and colleagues lose for you?” We included 7-point scales forthese questions that ranged from 1: “Not at all” to 7: “A lot.” The re-sponses to these three questions were highly correlated, and we aver-aged responses to the three questions to form an emotional costs scale(Cronbach’s alpha = 0.87).

5.1.4. Ethical judgmentTo understand consumers’ perceptions of the unethical nature of

auto insurance fraud, we asked, “In your opinion, how unethical is it forthe individual to have done what they did?” As before, we included a 7-point scale that ranged from 1: “Not at all” to 7: “A lot.”

5.2. Results

To test Hypothesis 1a, we compared perceptions of claims referred


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to law enforcement to industry rates of actual referrals of these cases(20.0%) using a one-sample t-test. Mean = 44.9% t(239) = 13.02,p < 0.001; d= 0.84; 95% CI [21.20, 28.63].1 For robustness, we alsoconducted a one-sample Wilcoxon Signed Rank Test and found thesame pattern of results. A summary of results by scenario appears inTable 3.

To test Hypothesis 1b, we compared perceptions of claims prose-cuted to the highest state-level rate of cases opened that we identified inStudy 3 (16.7%) using a one-sample t-test. Mean = 37% t(239) = 11.10, p < 0.001; d= 0.69; 95% CI [17.28, 24.73].2 As be-fore, we conducted a one-sample Wilcoxon Signed Rank Test for esti-mates of prosecutions and the found the same pattern of results. Asummary of results by scenario appears in Table 3.

To test Hypothesis 3a, we next analyzed responses to our emotionalcost questions. We found that respondents anticipated that they wouldfeel substantial levels of moral emotions if they had committed in-surance fraud. Average responses to our emotional cost questions were5.58 (SD = 1.52), which were substantially above the mid-point of thescale, t(239) = 16.05, p = 0.000; d= 1.04; 95% CI [1.39, 1.77]. Wealso found that respondents characterized the claimant’s behavior asunethical. The average response to our ethical judgment question was5.90 (SD = 1.46), which was substantially above the mid-point of thescale, t(239) = 20.18, p = 0.000; d= 1.30; 95% CI [1.71, 2.08].

To test the relationship between emotional costs, economic sanc-tioning, and ethical judgments associated with auto insurance fraud, weran correlation analyses (Table 4) and a hierarchical regression(Table 5). This regression reveals that consumers’ ethical judgments areclosely associated with their forecasts of their emotional reactions ra-ther than their forecasts of detection of engaging in this behavior. Thatis, respondents’ perceptions of morality appear to be guided by theiremotional reactions, rather than their calculated assessment of the costsof engaging in this behavior.

5.3. Discussion

In Studies 1, 2, and 3, we describe the auto insurance fraud sanc-tioning system. We find that this sanctioning system is surprisinglyweak. In this study, we focus on customer perceptions of the sanc-tioning system. We describe real cases that reflect the base-rate atwhich claims like these are advanced through the sanctioning system.

We contrast the outcomes of these cases with customer estimates,and we identify two key findings. First, customers substantially over-estimate the likelihood that fraud cases will be referred to law en-forcement and prosecuted once referred. Second, we identify sub-stantial costs that prior sanctioning system research in business orga-nizations has largely overlooked: emotional costs that are closelyassociated with perceptions of morality.

6. Study 5

In this study, we extend our investigation to consider customerperceptions of the government stage of the sanctioning system. Weinvestigate the extent to which customers misperceive the legal con-sequences associated with auto insurance fraud convictions. As in Study4, we also assess emotional costs and perceptions of immorality.

Table 3Study 4: percent likelihood that the claim was: (a) Referred to law enforcement, or (b) Prosecuted by law enforcement.

Note: “Referred” are cases referred from the auto insurance company to an outside investigative body. In Study 4, 20.0% of theclaims that were presented to participants were actually referred to law enforcement by the insurance company. “Prosecuted”are cases prosecuted with criminal charges. The actual prosecuted percentage presented is the highest annual percentage ofclaims opened for investigation of those that were referred in California (percentage calculated from the CA Annual report of theCommissioner, 2016).

Table 4Study 4: descriptive statistics and correlations.

Means S.D. 1 2 3

1. Expected investigation 44.87 29.582. Expected prosecution 37.00 29.30 0.59**

3. Emotional costs 5.58 1.52 0.02 0.004. Ethical judgment 5.90 1.46 0.04 −0.07 0.59**

Note. N= 240.** p < .01.

Table 5Study 4: hierarchical regression of expectations of detection and emotionalcosts on ethical judgments of auto insurance fraud.

Ethical Judgment

Variables Step 1 Step 2 Step 3

Expectations of investigation 0.04 0.12 0.10Expectations of prosecution −0.14 −0.13*Emotional costs 0.59***

R2 0.00 0.01 0.36ΔR2 0.01 0.35F 0.30 1.68 44.36***

Note: N= 240.* p < .05.*** p < .001.

1 The study had sufficient power (post hoc power = 100%).2 The study had sufficient power (post hoc power = 100%).


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6.1. Data analysis

We recruited 260 auto insurance customers (49.6% female; meanage = 37.3 years old) to participate in an online survey throughAmazon’s Mechanical Turk in exchange for an 80-cent payment. Weasked participants to read an actual auto insurance claim in which theclaimant was prosecuted and found guilty. After reading about theclaim, we asked participants to estimate the size of the fine, and thelength of incarceration. We also asked respondents questions aboutembarrassment and shame, perceptions of the morality of the claimant’sbehavior, and demographics.

We randomly assigned participants to read one of five claims fromthe Insurance Fraud Bureau of Massachusetts (2018). These claim de-scriptions include a detailed synopsis of the case. In addition, we hadprosecution outcome data (e.g., the magnitude of the fine, the length ofthe prison sentence) for these cases.

We selected claims that were flagged as suspicious, investigated bythe insurance organization, denied payment, referred to law enforce-ment, and prosecuted.

We include one of these summaries in Appendix B, and the re-maining summaries can be found on https://bit.ly/2C7Y6rj. We did notprovide respondents with the prosecution outcome information.

After respondents read the description about the case, we askedthem questions that corresponded to the following variables:

6.1.1. Expected fineFirst, we asked participants to estimate the magnitude of the fine;

“In the case you just read, if the individual was found guilty, how muchdo you think the fine was? (Your answer should be in $US dollars).”

6.1.2. Expected incarcerationSecond, we asked participants to assess the length of incarceration

associated with a guilty decision; “Based upon what you know aboutinsurance fraud prosecution, how long of a sentence would the courtorder for this individual? (Enter “0” if one of these units does notapply.)” We asked participants to fill in three boxes labeled: Years,Months, and Days.

6.1.3. Emotional costWe measured emotional cost using the same three items we used in

Study 4. As in Study 4, responses to these three questions were highlycorrelated, and we averaged responses to the three questions to form anemotional cost scale (Cronbach’s alpha = 0.79).

6.1.4. Ethical judgmentTo understand consumers’ perceptions of the unethical nature of

auto insurance fraud, we asked, “In your opinion, how unethical is it forthe individual to have done what they did?” As before, we included a 7-point scale for this question that ranged from 1: “Not at all” to 7: “Alot.”

We also asked demographic questions as well as questions similar tothose we included in Study 4.

6.2. Results

To test Hypothesis 2, we compared auto insurance consumers’perceptions about expected fines and incarceration to the actual out-comes of each of these five claims. For each of the five claims, the in-surance customers we surveyed over-estimated both the fine amountand the length of incarceration. We report the complete set of results inTable 6.

We find a similar pattern of results across our five claims. To il-lustrate these findings, we describe our results for Scenario 1. In thisclaim, the claimant was fined $500 and served 90 days in prison. Wecompared this outcome to consumers’ estimates using a one-sample t-test. Customers estimated that the claimant would be fined $5,139, t

(49) = 5.74, p < .001; d= 0.81; 95% CI [3006, 6388], and serve14.5 months in prison, t(48) = 4.90, p < .001; d= 0.70; 95% CI [6.2,15]. For each claim, we also conducted one-sample Wilcoxon SignedRank Test, and we found the same pattern of results and significancelevels.3

To test Hypothesis 3a, we next analyzed responses to our emotionalcost questions. We found that respondents anticipated that they wouldfeel substantial levels of emotion (embarrassment and shame) if theyhad committed insurance fraud. Average responses to our emotionalcost questions were 6.05 (SD = 1.15), substantially above the mid-point of the scale, t(259) = 28.88, p = 0.000; d= 1.78; 95% CI [1.91,2.19]. We also found that respondents perceived the claimant’s beha-vior to be unethical. On average, respondents rated the unethicality ofthe behavior to be 6.16 (SD = 1.23), which was substantially above themid-point of the scale, t(259) = 28.36, p = 0.000; d= 1.76; 95% CI[2.01, 2.31].

To test the relationship between emotional costs, economic sanc-tioning, and ethical judgments associated with auto insurance fraud, wefirst ran correlations (Table 7) and then ran a hierarchical regression(Table 8). For these analyses, we excluded responses for fines or in-carceration based upon criteria in our pre-registration (AsPredicted.org#9086) and reported results with the exclusions.4 This regression re-veals that consumers’ perceptions of ethicality are closely associatedwith their forecasts of their emotional reactions, but not closely relatedto their forecasts of economic consequences.

6.3. Discussion

As in Study 4, we investigate perceptions of the sanctioning system.In this study, we consider perceptions of the law enforcement stage ofsanctioning. We demonstrate, with real case descriptions, that in-surance customers systematically over-estimate the severity of punish-ments. This is true for both the amount of fines and the expected lengthof incarceration. In addition, as we find in Study 4, insurance customersanticipate substantial emotional costs to engaging in auto insurancefraud, and again we find that the magnitude of these emotional costs isclosely linked with perceptions of how unethical the claimants’ beha-vior is. To further explore the role of the deterrence effects of non-economic aspects of a sanctioning system, we designed an experimentthat independently manipulates both social and economic sanctions.

7. Study 6

Our first five studies describe the sanctioning system and identifyimportant relationships between sanctions, emotional costs, and moraljudgments. In this study, we extend our investigation and devote par-ticular attention to social sanctions. Social sanctions have been largelyoverlooked in sanctioning system research, but may play a critical rolein deterring fraud; social sanctions may make otherwise weak sanc-tioning systems strong. In this study, we independently manipulate bothsocial and economic sanctions.

3 Inspired by the review process, we conducted a separate study to rule-outpotential framing effects. We manipulated whether or not respondents esti-mated the likelihood that the claimant would ‘definitely [not] lose the case,’‘definitely [not] win the case’ or ‘definitely lose – definitely win the case.’ As inthis study, we find a main effect of overestimation across scenarios, and wefound no meaningful framing effects.

4 We excluded responses for expected fines and incarceration that were morethan 3 standard deviations from the mean. For Study 5 exclusions by scenariowere: Scenario 1 (fine = 1, prison = 2), Scenario 2 (fine = 1, prison = 1),Scenario 3 (fine = 1, prison = 2), Scenario 4 (fine = 1, prison = 1), Scenario 5(fine = 1, prison = 1). Taking a log of the data without exclusions producedresults with the same pattern.


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7.1. Data analysis

We recruited 395 auto insurance customers (52.4% female; meanage = 38.1 years old) to participate in an online survey throughAmazon’s Mechanical Turk in exchange for an 80-cent payment. Eachparticipant read details about a prosecuted automotive insurance claim

Table 6Study 5: expected and actual fines and incarceration

Table 7Study 5: descriptive statistics and correlations.

Means S.D. 1 2 3

1. Expected fine 6130.56 8887.842. Expected incarceration 12.46 17.80 0.16*3. Emotional costs 6.06 1.14 0.07 −0.014. Ethical judgment 6.16 1.23 0.05 −0.10 0.56***

N= 248, **p < .001.* p < .05.*** p < .0001.

Table 8Study 5: hierarchical regression of economic sanctions and emotional costs onethical judgments of auto insurance fraud.

Ethical Judgment

Variable Step 1 Step 2

Expected sanctionsExpected fine 0.07 0.03Expected incarceration −0.10 −0.10

Emotional Costs 0.56 ***

R2 0.01 0.32ΔR2 0.31***

F 1.55 38.38***

Note: N= 248.Consumers’ perceptions of how unethical auto insurance fraud is closely relatedto their perceptions of emotional costs rather than economic costs.

*** p < .001.


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and assessed the likelihood that another driver, familiar with the out-come of the case, would engage in a similar type of auto insurancefraud. We then asked participants to imagine that either the fine waslarger (doubled) or that the outcome of the case was posted online andto make a second set of judgements.

We randomly assigned participants to one of four conditions from a2(Claim Description) × 2(Increased Sanction: Fine Doubled v. OutcomePosted Online) design. In each condition, participants read one of twoclaims reported by the Insurance Fraud Bureau of Massachusetts (2018)that we used in Study 5 (Scenarios 2 and 4). These claims involved aneconomic fine ($855, $500) but no prison sentence. We include one ofthese summaries in Appendix C, and the remaining summaries can befound on https://bit.ly/2C7Y6rj. In the first description of these claims,we told participants that the outcome of the prosecution was con-fidential.

After reading details about the claim and the outcome, we askedparticipants to answer a number of questions. First, to assess deter-rence, we asked them to estimate the likelihood that another driver,familiar with the prosecution outcome, would file a false insuranceclaim like this one. Specifically, we asked, “How likely would this otherdriver be to file a false insurance claim?” on a slider (0: No Chance, 100:Certain).

Second, to assess emotional costs, we asked participants threequestions about how much embarrassment/shame/loss of respect theywould experience if this happened to them (0: “None at all” to 100:“The most extreme [emotion] one can feel”/“They would lose all re-spect for me”). As in Studies 4 and 5, responses to these three questionswere highly correlated, and we averaged responses to the three ques-tions to form an emotional cost scale (Cronbach’s alpha = 0.84). Werepeated these questions after we increased the sanction (economic orsocial) and the questions remained highly correlated (Cronbach’salpha = 0.88).

Third, to assess morality, we asked them to evaluate how unethicalthe claimant’s behavior was. Specifically, we asked, “In your opinion,how unethical is it for the individual to have done what they did?” on a

slider (0: “Not at all” to 100: “The most unethical thing I can imaginedoing.”).

After answering these questions, we asked participants to imaginethat the sanction was different. We told half of the participants that thefine had doubled (Economic Sanction) and the other half of the parti-cipants that the outcome was posted online (Social Sanction). After wedescribed the increase in the sanction, we asked respondents to providea second set of assessments to the same questions. That is, each parti-cipant answered the same set of questions twice—once in the base casewith a low, confidential fine, and a second time with either a higher,confidential fine or a low, public fine.

Finally, we asked participants a set of demographic questions si-milar to those we included in Studies 4 and 5.

7.2. Results

We find very similar results across both claim conditions. We reportsummary statistics in Table 9, and we report our results collapsed acrossthe two claim conditions.

7.2.1. Emotional costsAs in Studies 4 and 5 and supporting Hypothesis 3a, we find that the

prospect of insurance fraud is associated with high emotional costs.Compared to the midpoint of 50, participants reported elevated emo-tional costs in the Base condition (M = 76.61, t = 28.43, p = .000,d= 1.43), the Economic Sanction condition (M = 76.85, t = 18.75,p = .000, d= −1.33) and the Social Sanction condition (M = 83.69,t = 26.79, p = .000, d= 1.91). We also find that the increase in emo-tional costs relative to the Base condition is statistically significant forboth the Social Sanction condition (t = −9.01, p = .000) andEconomic Sanction condition (t = −3.50, p = .001).

7.2.2. Ethical judgmentConsistent with our findings in Studies 4 and 5 and supporting H3b,

we find that ethical judgments are highly correlated with emotional

Table 9

Panel A: study 6 descriptive statistics and correlations for base and social sanction conditions (N=197)

Means1 S.D. 1 2 3 4 5

1. Deterrence (Base)2 32.84 26.502. Deterrence (Social)2 21.64 25.58 0.73***

3. Emotional costs (Base) 77.75 17.96 −0.14 −0.144. Emotional costs (Social) 83.69 17.65 −0.25*** −0.29*** 0.87***

5. Ethical judgment (Base) 77.49 16.50 −0.05 −0.06 0.61*** 0.59***

6. Ethical judgment (Social) 79.76 16.51 −0.11 −0.11 0.63*** 0.64*** 0.89***

Panel B: study 6 descriptive statistics and correlations for base and economic sanction conditions (N = 198)

Means1 S.D. 1 2 3 4 5

1. Deterrence (Base)2 29.87 25.572. Deterrence (Economic)2 22.84 26.40 0.90***

3. Emotional costs (Base) 75.48 19.20 −0.19*** −0.17*4. Emotional costs (Economic) 76.85 20.15 −0.20*** −0.19** 0.96***

5. Ethical judgment (Base) 75.30 18.97 0.12 −0.08 0.46*** 0.43***

6. Ethical judgment (Economic) 76.21 19.61 −0.20** −0.14 0.50*** 0.48*** 0.93***

* p < .05.** p < .001.*** p < .0001.1 All measures on 0–100 scale.2 Deterrence is a measure of likelihood of engaging in auto insurance fraud. Lower numbers reflect greater deterrence.


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costs. This is true for the Social Sanction condition (Table 9, Panel A), inwhich ethical judgment is correlated with the emotional costs in theBase condition (r = 0.61, p = .000) and the Social Sanction condition(r = 0.64, p = .000). Similarly, in the Economic Sanction sample(Table 9, Panel B), ethical judgment is correlated with the emotionalcosts in the Base condition (r = 0.46, p = .000) and the EconomicSanction condition (r = 0.48, p = .000).

7.2.3. Social sanctionsSupporting Hypothesis 4 and as pre-registered on AsPredicted.org

#15928 we find that social sanctions, in this case publicizing the out-come of the prosecution, significantly curtails the likelihood of enga-ging in insurance fraud. We find a significant difference between thereported likelihoods in the Base condition (M = 32.84, SD = 26.50)and the Social Sanction condition (M = 21.64, SD = 25.58, t(197) = 8.13, p = .000; d= 0.43; 95% CI [8.4770. 13.9087]).

7.2.4. Economic sanctionsWe find that increased economic sanctions, in this case doubling the

fine, significantly curtails the likelihood of engaging in insurance fraud.We find a significant difference between the reported likelihoods in theBase condition (M = 29.87, SD = 25.57) and the Economic Sanctioncondition (M = 22.84, SD = 26.40, t(198) = 8.28, p = .00; d= 0.27;95% CI [5.3562, 8.7044]).

7.2.5. Contrasting social and economic sanctionsIn our investigation, we found that the social sanction we described

acted as a greater deterrent than the economic sanction we described (t(393) = 2.58, p = .01; d= 0.26; 95% CI [0.9857, 7.3395]). See Fig. 3for an overview of the contrasting effects. Though this result is

statistically significant, it may reflect differences in the base cases, ra-ther than meaningful treatment differences.

7.3. Discussion

In this study, we build on our finding that anticipated emotional andpsychological costs, specifically embarrassment, shame, and loss ofrespect, represent significant deterrents to engaging in fraud. In thisstudy, we extend our investigation to test the distinct deterrence effectsof both social and economic sanctions. We find that insurance custo-mers are sensitive to both social (e.g., making the fraud prosecutionpublic) and economic (e.g., doubling the fine) sanctions, and in thisstudy, we find that public sanctions acted as a greater deterrent thandoubling the fine. We are cautious, however, to infer that social sanc-tions are stronger than economic sanctions. For example, other eco-nomic sanctions (e.g., tripling rather than doubling the fine) mightyield a different result. Even so, our findings underscore the importantrole that social sanctions play in deterring fraud and further our un-derstanding of when otherwise weak sanctioning systems are strong.

8. General discussion

In this article, we describe organizational constraints that influencethe automotive insurance fraud sanctioning system, and we identifyhow objectively weak sanctioning systems may effectively deter fraud.We find that misperceptions of the economic sanctions and socialsanctions that attach emotional costs make the insurance fraud sanc-tioning system much stronger than the actual economic costs wouldsuggest. Across our studies, we demonstrate that consumers (a) mis-perceive auto insurance sanctions, (b) are responsive to social sanc-tions, and (c) associate high emotional costs with auto insurance fraud,which are tightly tied to their perceptions that fraud is unethical. In thissection, we consider implications of these findings, and we identifypromising directions for future research.

8.1. Weak sanctioning systems

Our investigation significantly advances our understanding ofsanctioning systems. By integrating data from an auto insurance com-pany, government agencies, and customers, we identify organizationalconstraints and psychological factors that profoundly influence the ef-ficacy of the auto insurance fraud sanctioning system. We demonstratethat the formal sanctioning system, characterized by detection rates,fines, and incarceration, is surprisingly weak. By interviewing fraudinvestigators, we identify organizational constraints, such as a profitmotive, public relations concerns, and resource constraints that limitthe effectiveness of the sanctioning system.

8.1.1. Misperceptions of detection and severityAlthough the actual sanctioning system is weak, we identify sys-

tematic differences between this actual sanctioning system and per-ceptions of this system. At every level that we measured, customersoverestimate the probability of detection and overestimate the magni-tude of the consequences of fraud. Our investigation is very differentfrom prior sanctioning investigations that have either presumed thatindividuals accurately perceive the sanctioning system or used experi-ments in which individuals are provided with a complete and accuraterepresentation of the sanctioning system (e.g., Tenbrunsel & Messick,1999). Our findings underscore the importance of understanding sub-jective perceptions of the sanctioning system, because these can departquite dramatically from reality.

If the likelihood of detection and the magnitude of losses were ac-curately perceived by consumers, the auto insurance industry mightexperience much higher rates of fraud that could trigger a fraud-pre-mium spiral: higher rates of fraud would increase premiums, which inturn might promote additional fraud, because high insurance premiums

Fig. 3. Study 6: likelihood of engaging in fraud based upon sanction type(N = 395).


13

http://AsPredicted.org

are often used as a justification for insurance fraud (Coalition AgainstInsurance Fraud, 2007). Quite possibly, the misperceptions we identifyin this work may dampen the fraud-premium spiral in the automotiveinsurance industry.

8.1.2. Emotional costsIn addition to misperceptions, we identify emotional costs of en-

gaging in insurance fraud. Specifically, we find that individuals an-ticipate feeling embarrassment and shame, and that these anticipatedfeelings may help to deter fraud. Quite possibly, firms could deriveparticularly significant benefits by broadcasting memorable—and em-barrassing—cases of detected insurance fraud. Across our studies of autoinsurance consumers, we identify strong emotional reactions to fraud.Perceptions of the emotional costs of engaging in fraud varied acrossdifferent sanctions (fines, incarceration, publicity), but the absolutelevels were very high across every sanction regime.

Anticipated embarrassment and shame are closely associated withassessments of morality. We call for future work to explore the psy-chology of emotions, and the relationships among emotional costs,economic costs, and perceptions of morality. Prior work has found thateconomic sanctions can exacerbate unethical behavior by triggering abusiness, rather than an ethics mindset (Tenbrunsel & Messick, 1999).Mulder (2009), however, asserts that severe economic sanctions elicit apunitive mindset and signal moral concerns. Reflecting upon Gneezyand Rustichini (2000) finding that parents become more likely to pick-up their children late from a daycare after the daycare imposed a fine,Mulder (2009) theorizes that the small size of the fine caused the par-ents to encode the fine as a price rather than a signal of moral concern.If the fine were very large, parents might encode the fine differently. Asour findings in Study 6 indicate, both the estimate of the magnitude ofthe penalty and the social sanction, as well as the associated emotionalcosts, matter.

8.1.3. Social sanctionsSimilar to emotional costs, social sanctions are an underexplored

aspect of sanctioning systems which explain when weak sanctioningsystems are strong. Our research suggests that social sanctions can in-crease emotional costs and deter fraud. In Study 6, we find that pub-licizing wrongdoing may be a cost-effective way for insurers and reg-ulators to curb consumer fraud. Our finding aligns with the work ofNelissen and Mulder (2013) who found that social sanctions were moreeffective than economic sanctions in reinforcing norms under condi-tions of imperfect monitoring in a lab study involving a social dilemma.In their study, the social sanction entailed expressing disapproval,which corresponds with the emotion of embarrassment. Thus, the socialsanction condition may have, in part, reflected an unmeasured emo-tional component. If we extend their findings to the context of autoinsurance fraud, which also suffers from imperfect monitoring, wewould expect social sanctions to have a more enduring effect on de-terrence than economic sanctions. Future work should carefully ex-amine the role of publicity in curbing auto insurance fraud. Re-searchers, however, should devote particular attention to the concernthat publicity may normalize fraudulent behavior and ultimately di-minish the emotional costs and stigma associated with being caught.

8.2. Limitations

Our datasets are unique because they entail real fraud investigationsoccurring within a for-profit organization. The breadth of our data,however, was limited by the timing of the investigations and the con-straints of the businesses. In Study 1, we relied on historical chron-ologies. In these fraud investigations, we could not observe the in-vestigative process directly nor were we able to systematicallyinterview the individuals involved in these investigations. We sought to

remedy this lack of richness with our interviews in Study 2, but futurework can gain greater insight into the investigative process.

Our empirical investigation extends our understanding of deceptiondetection both theoretically and practically but is subject to a numberof important limitations. Though our investigation is the first to in-corporate organizational, state, and customer data, our dataset is stillcharacterized by important omissions, such as our inability to identifycases of undetected fraud. We suspect that fraud is far more widespreadthan what investigators identify, and future work should explore thisshortcoming and test the generalizability of our model in other businesssettings. We also call for future work to investigate comparative staticsto contrast fraud behavior over time and across states to explore howexogenous factors shape perceptions of the sanctioning system.

We also note that in Studies 4, 5 and 6, our study participants’ re-sponses most likely reflect the perceptions of the average auto in-surance consumer, rather than career criminals. Those who engage inhard fraud (e.g., career criminals, organized crime rings) may have amore accurate perception of the underlying sanctioning system andmay be less prone to embarrassment and shame. Thus, our studyfindings are most likely to reflect “soft” fraud, which is fraud perpe-trated by typical consumers who opportunistically inflate their ex-penses. This is important, because soft fraud is the more pervasive,costly, and difficult to deter type of fraud (Viaene & Dedene, 2004). Wecall for future work to consider sanctioning systems for both “soft” and“hard” fraudsters. Quite possibly, managers will need to develop dif-ferent tools to detect and deter these very different forms of fraud.

8.3. Conclusion

Taken together, our findings reveal that psychological factors makethe auto insurance sanctioning system far more effective at deterringfraud than the formal system would suggest. Our findings make severalcontributions to the literature on organizational sanctioning systems byrevealing discrepancies between the objective and subjective sanc-tioning systems. First, our investigation highlights the importance ofunderstanding organizational objectives (e.g., profit and reputationalconcerns) in shaping sanctioning systems. In contrast to classical eco-nomic models of deterrence (e.g., Becker, 1968), ethical decisionmaking is guided by a rich set of factors, and our findings advance ourunderstanding of deterrence theory by focusing on the role of organi-zational constraints in the detection and sanctioning of unethical be-havior. Second, our findings identify the psychological forces thatstrengthen sanctioning systems including social sanctions, emotionalcosts, and misperceptions of detection rates and punishments. We callfor future research to build on our findings to explore the important rolethat these forces play in deterring unethical behavior. Importantly,these psychological forces may enable even weak sanctioning systemsto be far more effective than prior work suggests (Tenbrunsel &Messick, 1999). Insurance companies and state agencies have created asubstantial infrastructure to detect and punish insurance fraud. Thissanctioning system, however, is very costly and highly constrained byorganizational objectives. As a result, a very small fraction of cases areever investigated and an even smaller fraction are prosecuted. Whatultimately deters fraudsters, however, may have to do much more withperceptions of the sanctioning system and emotion than the reality.

Acknowledgments

We are indebted to Alex Hirsch for his excellent research assistanceand Bill Laufer for his guidance throughout the research project. Thismanuscript benefited greatly from thoughtful reviewer feedback andthe editorial guidance of Lamar Pierce and Shaul Shalvi. We also thankthe Wharton Behavioral Laboratory for support.


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Appendix A

Study 4, Claim 4An individual submitted an auto insurance claim for an incident in which their vehicle was struck by an unknown object, blowing out both

front tires. The individual claimed that the incident occurred at 12:15am on December 12th, and that they had the vehicle towed to a friend'shouse. The police were not called, and no accident report was fled.

An internal investigation conducted by the insurance company revealed that the individual had not paid their insurance premium. As aresult of not paying their premium, a notice was mailed to the individual on December 1st informing the individual that the policy would beterminated on December 12th. This would make the individual's claim invalid as it was for an incident that occurred after the policy had beenterminated.

During the investigation, the insurance company learned that individual had called them on December 11th to inquire about the termi-nation notice and to arrange a payment to maintain their insurance. The individual claimed to have mailed the payment on December 11th,after the phone call. However, the payment was post-marked December 15.

In a follow-up recorded statement made by the individual, the individual changed their story. In the second statement, the individualclaimed that the accident happened before midnight on December 10th. This change to the date of the incident conflicted not only with theindividual's previous statement, but also with a statement given by a witness who picked up the individual from the scene of the incident. Thewitness recalled picking up the individual at 10:30 pm on December 11th.

The findings of the insurance company’s investigation were that the statements given about the incident were inconsistent. Because thestatements were inconsistent and because the insurance policy had expired when the individual originally stated the accident had occurred, theinsurance company denied the claim.

Appendix B

Study 5, Claim 1An individual reported the theft of their Saab to police and to their insurance company. Police conducted a search of the Saab’s Vehicle

Identification Number (VIN), a unique number that only that particular car has, in the National Motor Vehicle Title Information SystemDatabase. The search revealed that the Saab was crushed by a recycling company that crushes vehicles for scrap metal. According to the report,the recycling company obtained the Saab on February 6 from an auto mall before it was crushed. The police investigation revealed that theowner of the auto mall wrote a check for $650 to the individual, indicating that the vehicle had been sold, not stolen. In the remark section ofthe check “02 Saab” was written. The owner of the auto mall also identified the individual from a photo line-up as the person who sold him theSaab.

Appendix C

ToyotaAn individual submitted an auto insurance claim stating that their vehicle was struck by another vehicle, which then fled the scene. They

reported that the collision caused damage to the entire driver's side of their vehicle and caused them to veer out of control. The individual gavethe name of a witness to the accident.

A forensic examination of the vehicle found that the damage was not consistent with being struck by another vehicle, but instead was theresult of hitting a guardrail while moving forward. The investigators also found that the witness's phone number belonged to a friend of theindividual, and that the address provided for the witness was fictitious.Lexus

An individual reported to police and to their insurance company that their vehicle was hit while it was parked. The vehicle had sufferedsignificant damage to the driver's side. In the report, the individual stated that the vehicle had been parked the day before they noticed thedamage, and that they had notified police as soon as they saw the damage.

An independent accident reconstruction expert found the damage to the vehicle was not consistent with the individual's story. In additionto the nature of the damage, the investigator noted that the airbags had deployed. When the vehicle is parked and the ignition is in the offposition, airbags are unable to deploy. Further, the vehicle's Event Data Recorder showed that the vehicle was traveling at a constant speed of37 mph when the collision occurred and that the airbags had deployed when the vehicle had suddenly changed speed.

According to the individual who owned the vehicle, they were the vehicle's sole operator. They maintained that they did not have anaccident while driving the vehicle, but that the vehicle had been locked and they had been in possession of all of the keys to the vehicle theentire time.

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https://www.propertycasualty360.com/2017/02/03/go-pro-video-leads-to-conviction-of-auto-insurance/

https://www.propertycasualty360.com/2017/02/03/go-pro-video-leads-to-conviction-of-auto-insurance/















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