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Detecting deception in insurance claims - How effective are verifiability approach and model statement?

  • July 2018
DOI:10.13140/RG.2.2.13558.50243
  • Conference: Annual meeting of the European Association of Psychology and Law
Authors:
André Körner at Medical School Berlin
André Körner
Marc Urban
Marc Urban
Marc Urban
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Abstract

Deception detection can be a difficult task. Based on mere subjective evaluations, people can hardly distinguish between true and fabricated stories. This is even the case for professionals, whose accuracy rates only minimally outperform chance level (Vrij, 2008). Content-based techniques of deception detection, i.e. CBCA, are able to discriminate between true and fabricated statements with an accuracy rate of approximately 70% (Oberlader et al., 2016). The lately introduced verifiability approach (VA; Nahari, Vrij, & Fisher, 2014) holds promising results: As a verbal veracity tool, this approach has proven to be a highly diagnostic instrument for detecting deception in insurance settings. At least in conjunction with an information protocol (IP), the VA correctly classifies true versus fake insurance claims with a discrimination rate of 80%. If the IP is additionally supplemented with a model statement (MS), the VA even attains a discrimination rate of 90% (Harvey, Vrij, Leal, Lafferty, & Nahari, 2017). Trying to replicate and extend previous lab results, we conduct a pre-registered online study using a 2 (veracity: truth vs. lie) x 3 (additional information given to participants: IP vs. MS vs. IP + MS) between-subjects design. Specifically, we want to test whether the VA combined with IP meets its expected high diagnostic value in distinguishing between true and fabricated reports. Furthermore, we will test the effect on detection rates when adding a mere MS without IP, or when a full combination is presented. We will critically discuss the results of our study and outline potential practical applications.
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www.allpsy2.de1
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
XXVIII. Annual Conference of the European
Association of Psychology and Law
Turku (FIN), June 26-29, 2018
Detecting Deception in Insurance Claims –
How Effective are Verifiability Approach and
Model Statement?
André Körner & Marc Urban
Chemnitz University of Technology, Germany, Chair for General Psychology and Biopsychology
www.allpsy2.de2
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Agenda
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
Implications, outlook
Methods, Coding Procedure
Results
Short introduction to insurance claims, VA
www.allpsy2.de3
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Agenda
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
Implications, outlook
Short introduction to insurance claims, VA
Methods, Coding Procedure
Results
www.allpsy2.de4
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
•estimated total loss of 4-5 billion EUR per year due to fraudulent
claims (Gesamtverband der Deutschen Versicherungswirtschaft e.V., 2017)
•insurance fraud is seen as a peccadillo (Knoll, 2011)
•easy opportunity for insurance fraud
in liability insurance and household insurance
(John, 2011)
•often incorrect knowledge about the product “insurance” (Knoll, 2011)
Why does insurance fraud (in Germany) matters?
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de5
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Scientific findings for verbal reports –thus far
•without additional tools: no reliable discrimination between
deception and truth by lay persons or professionals
(Bond & DePaulo, 2006; Vrij, 2008)
•Criteria Based Contents Analysis (CBCA) or Reality Monitoring (RM):
Discrimination Accuracy ≈ 70% (Oberlader et al., 2016)
•Verifiability Approach (Nahari, Vrij & Fischer, 2014) in an insurance setting:
with Information Protocol (IP): Discrimination Accuracy = 80%
(Harvey, Vrij, Nahari, & Ludwig, 2016)
with IP and Model Statement (MS): Discrimination Accuracy = 90%
(Harvey, Vrij, Leal, Lafferty, & Nahari, 2017)
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de6
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
•Verbal veracity tool: examines frequency of checkable details in statements
truth-tellers
liars withhold
checkable
information
tell it all
provide many details to
appear credible
vs.
minimize risk
to get caught lying
Information
Management Dilemma
Different verbal strategies
What is the Verifiability Approach (VA)?
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de7
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
How to ‘stretch’ the difference between lie & truth
Information Protocol
•informs about the importance of including checkable information
(Nahari et al. 2014b)
•components: reporting, analysis, definition, falsifiability (Harvey et al. 2017)
Verifiable Details are:
i) Activities carried out with identifiable or named persons who the
interviewer can consult
ii) Activities that have been witnessed by identifiable or named persons
who the interviewer can consult
iii) Activities that the interviewee believes may have been captured on CCTV
iv) Activities that may have been recorded or documented, such as using
debit cards, mobile phones, or computers.
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de8
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
How to ‘stretch’ the difference between lie & truth
Information Protocol
•informs about the importance of including checkable information
(Nahari et al. 2014b)
•components: reporting, analysis, definition, falsifiability (Harvey et al. 2017)
Model Statement
•detailed example of an unrelated topic (Leal, Vrij, Warmelink, Vernham & Fisher, 2015)
•reference point for richness of detail and length
•in conjunction with IP: better understanding of verifiable details
•translated version (Harvey et al. 2017): 378 words, 49 verifiable details
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de9
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
•1: truth tellers will report more verifiable details than liars.
•2: liars will report more unverifiable details than truth tellers.
•3: truth tellers will show a higher percentage of verifiable details
(verifiable details/ total details).
•4: When using the predictor “percentage of verifiable details”,
the Verifiability Approach achieves a higher diagnostic efficacy in the
condition “IP + MS” than in the condition “IP”.
•Pre-Registration: www.osf.io/v3at5
Hypotheses
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de10
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Agenda
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
Implications, outlook
Short introduction to insurance claims, VA
Results
Methods, Coding Procedure
www.allpsy2.de11
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Design
•
2 (Veracity: truth vs lie) x 3 (Intervention: IP vs IP+MS vs MS)
between-subjects design
Procedure
•
online-study (PC or Laptops)
•
Incentives
(3 x 50,- EUR Amazon voucher, course credit, SurveyCircle points)
•
quasi-experimental: veracity
(“Has any item of yours, worth between 100 and 1000,- EUR, been lost,
damaged or stolen in the last three years?”)
•
imagine to submit a genuine or fabricated insurance claim
•
randomized: intervention
•
task: writing free report, being as detailed as possible.
Convince the human analyst of being truthful.
•
… demographic information, post-interview-questionnaire
Methods: Designing an VA online study
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de12
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Methods: (Quasi)-experimental design
Randomization
Intervention
IP IP + MS MS
Quasi-experimental
Veracity
Truth 20 20 20
Lie 20 20 20
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de13
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Analyzed
Sample
•
N= 120 persons; 76 female, 42 males, 2 not specified
•
Age between 18 and 66 years, Mage = 26.37 (SD = 7.97)
•
71.7% students, 25% employed, 0.8% pupils, 2.5 % not specified
Coding:
procedure
and
training
1. identifying “information units” using RM criteria
(Vrij, 2008)
•perceptual details, spatial details, temporal details
•activities were considered perceptual details
2. categorization into verifiable vs. unverifiable details
•criteria as presented in the IP
coder training:
•consulting with experienced German expert
•content: background literature RM, written definitions and
examples, practice with sample texts, discussions and further
differentiations.
Methods: Sample and subsequent coding
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de14
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Usage of MAXQDA as a coding tool
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de15
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Agenda
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
Results
Short introduction to insurance claims, VA
Implications, outlook
Methods, Coding Procedure
www.allpsy2.de16
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Veracity
manipulation
check
•
Truthful claimants: high overall truthful rating (11p scale)
(M = 9.20, SD = 2.50)
•
Deceptive claimants had significantly lower truthful ratings
(M = 4.08 , SD = 3.32 )
•
t(109.760) = 9.54, p < 0.001, d = 1.74
Motivation
•
Reported motivation of the participants was high (7p scale)
(M = 5.63, SD = 1.04)
•
No main effect for Veracity or Intervention; no interaction effect
Word count
•
On average: 202 words (SD = 126); Mfabricated = 250; Mtruth = 176
•
Main effect for veracity: F(1,114) = 6.22, ηp2= .05, p = .014
•
Main effect for intervention: F(2, 114) = 6.846, ηp2= .11, p = .002
•
No significant Veracity x Intervention interaction effect.
Manipulation Checks
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de17
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Results & trends after coding (thus far)
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
IP IP+MS MS
truth false truth false truth false
N
o of details 17.15 (8.71)
21.05 (9.85)
Verifiable
details
9.50 (9.47)
Non
-verifiable
details
7.65 (6.81) 4.85 (5.58) 7.05 (8.11)
verifiable details/
total details
0.48 (0.41) 0.69 (0.36) 0.64 (0.37) 0.56 (0.32) 0.61 (0.36) 0.50 (0.39)
Note. All values are Means (SD) for the coded texts in the given cells.
www.allpsy2.de18
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
0. Truth tellers do NOT tell more details - overall
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
0
10
20
30
40
IP IP+MS MS
mean frequence
number of details TOTAL true
false
Veracity: F = 3.04 ηp2= .026
Intervention: F = 7.63* ηp2= .118
A x B: F = 2.38 ηp2= .040
www.allpsy2.de19
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
1. Truth tellers do NOT give more verifiable details - overall
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
0
10
20
30
40
IP IP+MS MS
mean frequence
number of details - VERIFIABLE true
false
Veracity: F = 0.87 ηp2= .008
Intervention: F = 1.70 ηp2= .030
A x B: F = 1.38 ηp2= .024
www.allpsy2.de20
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
2. Liars do NOT give more non-verifiable details
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
Veracity: F = 1.14 ηp2= .010
Intervention: F = 3.70* ηp2= .061
A x B: F = 2.44 ηp2= .041
0
10
20
30
40
IP IP+MS MS
mean frequence
number of details - NON-VERIFIABLE true
false
www.allpsy2.de21
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
3. Truth tellers do NOT show a higher percentage of verifiable details
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
0
1
IP IP+MS MS
mean frequence
verifiable details / total details true
false
Veracity: F = 0.00 ηp2= .000
Intervention: F = 0.17 ηp2= .003
A x B: F = 2.20 ηp2= .037
www.allpsy2.de22
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Agenda
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
Implications, outlook
Short introduction to insurance claims, VA
Methods, Coding Procedure
Results
www.allpsy2.de23
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Scientific findings –where to go?
•VA with it’s various types is a promising tool
for lie detection in insurance claims
•At least in German: VA is a tricky thing?
•Rather artificial design: Verifiable details may lead to conviction in real life
•Wait for final coding by decision
•Pre-Registration is a must have
•Maybe highly dependent on language
•Need for multi-center studies, normalize coding procedure
•Chance for specific algorithm for a first screening
(e.g. Srour, Py, & Maillot in this session)?
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
www.allpsy2.de24
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Thanks for your attention
http://imgur.com/ccZ1F
XXVIII. Annual Conference of the European
Association of Psychology and Law
Turku (FIN), June 26-29, 2018
www.allpsy2.de25
André Körner & Marc Urban∙General Psychology and Biopsychology ∙Chemnitz University of Technology, Germany
Sources
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Harvey, A. C., Vrij, A., Leal, S., Lafferty, M., & Nahari, G. (2017). Insurance based lie detection: Enhancing the verifiability approach with a model
statement component. Acta psychologica, 174, 1–8. https://doi.org/10.1016/j.actpsy.2017.01.001
Harvey, A. C., Vrij, A., Nahari, G., & Ludwig, K. (2016). Applying the Verifiability Approach to insurance claims settings: Exploring the effect of the
information protocol. Legal and Criminological Psychology, 22(1), 47–59. https://doi.org/10.1111/lcrp.12092
John, K. (Stand 2011). Versicherungsbetrug: aktuelle Entwicklungen, Muster und ihre Abwehr. Pressekonferenz des Gesamtverbandes der
Deutschen Versicherungswirtschaft e.V. Retrieved from http://www.gdv.de/wp-
content/uploads/2011/11/PK_Versicherungsbetrug_2011_Praes1_Versicherungsbetrug_in_Deutschland_GfK_KarstenJohn_n4.pdf
Gesamtverband der Deutschen Versicherungswirtschaft e.V. 2017
Knoll, J. (2011). Management von Betrugsrisiken in Versicherungsunternehmen (1. Aufl. 2011). Baden-Baden: Nomos.
https://doi.org/10.5771/9783845232874
Leal, S., Vrij, A., Warmelink, L., Vernham, Z., & Fisher, R. P. (2015). You cannot hide your telephone lies: Providing a model statement as an aid to
detect deception in insurance telephone calls. Legal and Criminological Psychology, 20(1), 129–146. https://doi.org/10.1111/lcrp.12017
Nahari, G., Vrij, A., & Fisher, R. P. (2014a). Exploiting liars' verbal strategies by examining the verifiability of details. Legal and Criminological
Psychology, 19(2), 227–239. https://doi.org/10.1111/j.2044-8333.2012.02069.x
Nahari, G., Vrij, A., & Fisher, R. P. (2014b). The Verifiability Approach: countermeasures facilitate its ability to discriminate between truths and
lies. Applied Cognitive Psychology, 28(1), 122–128. https://doi.org/10.1002/acp.2974
Oberlader, V. A., Naefgen, C., Koppehele-Gossel, J., Quinten, L., Banse, R., & Schmidt, A. F. (2016). Validity of content-based techniques to
distinguish true and fabricated statements: A meta-analysis. Law and human behavior, 40(4), 440–457.
https://doi.org/10.1037/lhb0000193
Vrij, A. (2008). Detecting lies and deceit: Pitfalls and opportunities (2nd ed.). Wiley series in the psychology of crime, policing and law. Chichester,
England, Hoboken, NJ: John Wiley & Sons.
Detecting Deception in Insurance Claims
Verifiability Approach and Model Statement
... To date, there have been 17 published studies using a MS and two studies reported at conferences (Hirn et al., 2012;Körner & Urban, 2018). One study used pairs of participants, and although the MS appeared useful for facilitating lie-detection, this has only been tested once . ...
... This combination was later retested by Körner and Urban (2018) ...
... The studies in which a MS enhanced lie-detection, researchers employed highly subjective measures, such as plausibility (see Leal et al., 2015), as well as measures that are difficult for investigators to determine, such as peripheral information ; for failure to replicate see; Leal et al., 2019a), and common knowledge details (Vrij et al., 2018b). Harvey et al. (2017) established that combining a MS containing verifiable information, in conjunction with the IP (of the verifiability approach) appeared effective, though this needs to be more robustly tested, as this effect has failed to be replicated (Bogaard et al., 2020;Körner & Urban, 2018). One of the key problems with these comparisons are that the researchers have all used different MS scripts, making any accurate replication-and meaningful comparison-difficult. Future research could more robustly examine the differences between the MS scripts and could provide a collection of such scripts for more independent replications to take place. ...
A critical analysis of the Model Statement literature: Should this tool be used in practice?
Article
Full-text available
  • Oct 2020
Investigators need to elicit detailed statements from interviewees to find potential leads, whilst simultaneously judging if a statement is genuine or fabricated. Researchers have proposed that the Model Statement (MS) can both (a) increase information elicitation from interviewees and (b) amplify the verbal differences between liars and truth tellers, thereby enhancing lie‐detection accuracy. Based upon a critical analysis of the MS literature, we argue that this tool is not currently ready for practical usage, as its utility has not been fully established. We highlight a diverse range of existing MS scripts, and a greater diversity in the dependent measures examined in conjunction with this tool. More robust replications of these procedures are needed. We also highlight why some measures of verbal content may not be suitable as outcome measures and suggest that new research could use the well‐established reality monitoring criteria to allow for standardisation across studies.
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Discriminating deceptive from truthful statements using the Verifiability Approach: A Meta‐analysis
Article
Full-text available
  • Dec 2020
The Verifiability Approach predicts that truth tellers will include details that can be verified by the interviewer, whereas liars will refrain from providing such details. A meta‐analysis revealed that truth tellers indeed provided more verifiable details (k = 28, d = 0.49, 95% CI [0.25; 0.74], BF10 = 93.28), and a higher proportion of verifiable details (k = 26, d = 0.49 95% CI: 0.25, 0.74, p < .001, BF10 = 81.49) than liars. We found no evidence that liars would include more unverifiable details than truth tellers (k = 20, d = −0.31, 95% CI [−0.02; 0.64], BF10 = 1.12) Moderator analysis revealed the verifiable detail effect was substantially larger when the statement is the suspect's alibi, but smaller when an incentive to appear credible was used. Our findings support the main prediction behind the Verifiability Approach, but also stress the need for larger sample sizes and independent replications.
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Validity of Content-Based Techniques to Distinguish True and Fabricated Statements: A Meta-Analysis
Article
Full-text available
  • Jul 2016
Within the scope of judicial decisions, approaches to distinguish between true and fabricated statements have been of particular importance since ancient times. Although methods focusing on “prototypical” deceptive behavior (e.g., psychophysiological phenomena, nonverbal cues) have largely been rejected with regard to validity, content-based techniques constitute a promising approach and are well established within the applied forensic context. The basic idea of this approach is that experience-based and non-experience-based statements differ in their content-related quality. In order to test the validity of the most prominent content-based techniques, Criteria-Based Content Analysis (CBCA) and Reality Monitoring (RM), we conducted a comprehensive meta-analysis on English- and German-language studies. Based on a variety of decision criteria, 56 studies were included revealing an overall effect size of g = 1.03 (95% CI [0.78, 1.27], Q = 420.06, p < .001, I² = 92.48%, N = 3429). There was no significant difference in the effectiveness of CBCA and RM. Additionally, we investigated a number of moderator variables such as characteristics of participants, statements, and judgment procedures, as well as general study characteristics. Results showed that the application of all CBCA criteria outperformed any incomplete CBCA criteria set. Furthermore, statement classification based on discriminant functions revealed higher discrimination rates than decisions based on sum scores. Finally, unpublished studies showed higher effect sizes than studies published in peer-reviewed journals. All results are discussed in terms of their significance for future research (e.g., developing standardized decision rules) and practical application (e.g., user training, applying complete criteria set).
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Accuracy of Deception Judgments
Article
Full-text available
  • Feb 2006
We analyze the accuracy of deception judgments, synthesizing research results from 206 documents and 24,483 judges. In relevant studies, people attempt to discriminate lies from truths in real time with no special aids or training. In these circumstances, people achieve an average of 54% correct lie-truth judgments, correctly classifying 47% of lies as deceptive and 61% of truths as nondeceptive. Relative to cross-judge differences in accuracy, mean lie-truth discrimination abilities are nontrivial, with a mean accuracy d of roughly .40. This produces an effect that is at roughly the 60th percentile in size, relative to others that have been meta-analyzed by social psychologists. Alternative indexes of lie-truth discrimination accuracy correlate highly with percentage correct, and rates of lie detection vary little from study to study. Our meta-analyses reveal that people are more accurate in judging audible than visible lies, that people appear deceptive when motivated to be believed, and that individuals regard their interaction partners as honest. We propose that people judge others' deceptions more harshly than their own and that this double standard in evaluating deceit can explain much of the accumulated literature.
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Insurance based lie detection: Enhancing the verifiability approach with a model statement component
Article
  • Mar 2017
Purpose: The Verifiability Approach (VA) is verbal lie detection tool that has shown promise when applied to insurance claims settings. This study examined the effectiveness of incorporating a Model Statement comprised of checkable information to the VA protocol for enhancing the verbal differences between liars and truth tellers. Method: The study experimentally manipulated supplementing (or withholding) the VA with a Model Statement. It was hypothesised that such a manipulation would (i) encourage truth tellers to provide more verifiable details than liars and (ii) encourage liars to report more unverifiable details than truth tellers (compared to the no model statement control). As a result, it was hypothesized that (iii) the model statement would improve classificatory accuracy of the VA. Participants reported 40 genuine and 40 fabricated insurance claim statements, in which half the liars and truth tellers where provided with a model statement as part of the VA procedure, and half where provide no model statement. Results: All three hypotheses were supported. In terms of accuracy, the model statement increased classificatory rates by the VA considerably from 65.0% to 90.0%. Conclusion: Providing interviewee's with a model statement prime consisting of checkable detail appears to be a useful refinement to the VA procedure.
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Applying the Verifiability Approach to insurance claims settings: Exploring the effect of the information protocol
Article
  • Dec 2016
Purpose Lie detection in insurance claim settings is difficult as liars can easily incorporate deceptive statements within descriptions of otherwise truthful events. We examined whether the Verifiability Approach ( VA ) could be used effectively in insurance settings. According to the VA , liars avoid disclosing details that they think can be easily checked, whereas truth tellers are forthcoming with verifiable details. Method The study experimentally manipulated notifying claimants about the interviewer's intention to check their statements for verifiable details (the ‘Information Protocol’). It was hypothesized that such an instruction would (1) encourage truth tellers to provide more verifiable details than liars and to report identifiable witnesses who had witnessed the event within their statements, and (2) would enhance the diagnostic accuracy of the VA . Participants reported 40 genuine and 40 fabricated insurance claim statements, in which half the liars and truth tellers were notified about the interviewer's intention to check their statements for verifiable details. Results Both hypotheses were supported. In terms of accuracy, notifying claimants about the interviewer's intention to check their statements for verifiable details increased accuracy rates from around chance level to around 80%. Conclusion The VA , including the information protocol, can be used in insurance settings.
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You cannot hide your telephone lies: Providing a model statement as an aid to detect deception in insurance telephone calls
Article
Deception research regarding insurance claims is rare but relevant given the financial loss in terms of fraud. In Study 1, a field study in a large multinational insurance fraud detection company, truth telling mock claimants (N = 19) and lying mock claimants (N = 21) were interviewed by insurance company telephone operators. These operators classified correctly only 50% of these truthful and lying claimants, but their task was particularly challenging: Claimants said little, and truthful and deceptive statements did not differ in quality (measured with Criteria-Based Content Analysis [CBCA]) or plausibility. In Study 2, a laboratory experiment, participants in the experimental condition (N = 43) were exposed to an audiotaped truthful and detailed account of an event that was unrelated to insurance claims (a day at the motor races). The number of words, quality of the statement (measured with CBCA), and plausibility of the participants' accounts were compared with participants who were not given a model statement (N = 40). The participants who had listened to the model statement provided longer statements than control participants, truth tellers obtained higher CBCA scores than liars, and only in the model statement condition did truth tellers sound more plausible than liars. Providing participants with a model statement is thus an innovative and successful tool to elicit cues to deception. Providing such a model has the potential to enhance performance in insurance call interviews, and, as we argue, in many other interview settings.
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The Verifiability Approach: Countermeasures Facilitate its Ability to Discriminate Between Truths and Lies
Article
SUMMARY According to the verifiability approach, liars tend to provide details that cannot be checked by the investigator and awareness of this increases the investigator's ability to detect lies. In the present experiment, we replicated previous findings in a more realistic paradigm and examined the vulnerability of the verifiability approach to countermeasures. For this purpose, we collected written statements from 44 mock criminals (liars) and 43 innocents (truth tellers), whereas half of them were told before writing the statements that the verifiability of their statements will be checked. Results showed that ‘informing’ encouraged truth tellers but not liars to provide more verifiable details and increased the ability to detect lies. These findings suggest that verifiability approach is less vulnerable to countermeasures than other lie detection tools. On the contrary, in the current experiment, notifying interviewees about the mechanism of the approach benefited lie detection. Copyright © 2013 John Wiley & Sons, Ltd.
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Exploiting liars' verbal strategies by examining unverifiable details
Article
  • Oct 2012
Background We examined the hypothesis that liars will report their activities strategically and will, if possible, avoid mentioning details that can be verified by the investigator. MethodA total of 38 participants wrote a statement in which they told the truth or lied about their activities during a recent 30-minute period. Two coders counted the frequency of occurrence of details that can be verified and that cannot be verified. ResultsLiars, compared with truth tellers, included fewer details that can be verified and an equal number of details that cannot be verified in their statement, and the ratio between verifiable and unverifiable details was smaller in liars compared with truth tellers. High percentages of truth tellers and liars were classified correctly based on the frequency counting of verifiable details (79%) or the ratio between verifiable and unverifiable details (71%). Those percentages were higher than the percentage that could be classified correctly (63%) based on verifiable and unverifiable detail combined. We compared our verifiability approach with other theoretical approaches as to why differences in detail between truth tellers and liars emerge.
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Versicherungsbetrug: aktuelle Entwicklungen, Muster und ihre Abwehr
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  • K John
John, K. (Stand 2011). Versicherungsbetrug: aktuelle Entwicklungen, Muster und ihre Abwehr. Pressekonferenz des Gesamtverbandes der Deutschen Versicherungswirtschaft e.V. Retrieved from http://www.gdv.de/wpcontent/uploads/2011/11/PK_Versicherungsbetrug_2011_Praes1_Versicherungsbetrug_in_Deutschland_GfK_KarstenJohn_n4.pdf Gesamtverband der Deutschen Versicherungswirtschaft e.V. 2017
Management von Betrugsrisiken in Versicherungsunternehmen
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Knoll, J. (2011). Management von Betrugsrisiken in Versicherungsunternehmen (1. Aufl. 2011). Baden-Baden: Nomos. https://doi.org/10.5771/9783845232874
Wiley series in the psychology of crime, policing and law
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Vrij, A. (2008). Detecting lies and deceit: Pitfalls and opportunities (2nd ed.). Wiley series in the psychology of crime, policing and law. Chichester, England, Hoboken, NJ: John Wiley & Sons.