Risk factors for fraud victimization: The role of socio-demographics, personality, mental, general, and cognitive health, activities, and fraud knowledge
| Published date | 01 September 2024 |
| DOI | http://doi.org/10.1177/02697580231215839 |
| Author | Luka Koning,Marianne Junger,Bernard Veldkamp |
| Date | 01 September 2024 |
https://doi.org/10.1177/02697580231215839
International Review of Victimology
2024, Vol. 30(3) 443 –479
© The Author(s) 2023
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/02697580231215839
journals.sagepub.com/home/irv
Risk factors for fraud
victimization: The role of socio-
demographics, personality,
mental, general, and cognitive
health, activities, and fraud
knowledge
Luka Koning
University of Twente, The Netherlands
Marianne Junger
University of Twente, The Netherlands
Bernard Veldkamp
University of Twente, The Netherlands
Abstract
Fraud against individuals is a major and growing problem. Understanding why some people fall
victim to fraud, while others do not, is crucial in developing effective prevention strategies.
We therefore studied the effect of socio-demographics, personality traits, mental, general,
and cognitive health, routine Internet activities, and prior fraud knowledge on general fraud
victimization, susceptibility to fraud attempts, and exposure to fraud attempts. We modeled data
from a Dutch fraud victimization survey, using an exhaustive fraud taxonomy and a representative
sample for which an elaborate set of historical background variables were available. Results show
that there is no clear personality or other profile of those most at risk for fraud, except for having
low self-control, having a non-Western, immigrant background or being a frequent Internet user.
Improving fraud knowledge could be an effective way to prevent fraud victimization by reducing
susceptibility to attempts.
Keywords
Fraud, victimization, susceptibility, exposure, personality, risk factors
Corresponding author:
Luka Koning, University of Twente, Drienerlolaan 5, 7500 AE Enschede, The Netherlands.
Email: l.koning@utwente.nl
1215839IRV0010.1177/02697580231215839International Review of VictimologyKoning et al.
research-article2023
Article
444International Review of Victimology 30(3)
Introduction
Since the end of the last century, general statistics on registered crime and victimhood have dropped
(Blumstein et al., 2000; De Jong, 2018; Hopkins, 2016; Van Dijk et al., 2012). This phenomenon
has been dubbed the ‘crime drop’. When all types of crimes are considered, this decrease holds true
and persists. However, a different picture can be seen when crime data are inspected more specifi-
cally, for instance, when focusing on fraud.
Fraud statistics across the globe, namely, have shown an increase in recent years, causing bil-
lions ($) of damage (Hine, 2021; PwC, 2022). New peaks in registered fraud have been seen in,
for example, the United States (FTC, 2022a), the United Kingdom (ONS, 2022), and across
Europe (EPC, 2022; Kemp et al., 2020), part of which may be explained due to a surge related to
the coronavirus disease (COVID)-19 pandemic (Zhang et al., 2022). These increases form a wor-
rying trend, as fraud causes considerable damage to society. If nothing changes, the problem may
only become worse.
Fraud can be defined as ‘intentionally and knowingly deceiving [a] victim by misrepresent-
ing, concealing, or omitting facts about promised goods, services, or other benefits and conse-
quences that are non-existent, unnecessary, never intended to be provided, or deliberately
distorted for the purpose of monetary gain’ (Beals et al., 2015: 7). It can be divided into fraud
targeting organizations and fraud targeting individuals. These two major categories of fraud
have distinct forms (Beals et al., 2015); thus, investigations into them also require vastly differ-
ent research methods. While both are highly relevant, the current research focuses on fraud
against individuals.
An important question in research on fraud against individuals (or: ‘consumer fraud’, ‘personal
fraud’, ‘individual fraud’) is why some people fall victim, while others do not. Answering this
question is key for effective and efficient measures that prevent victimization (Burke et al., 2022;
NFA, 2011: 12). The goal of the current study is to contribute to that goal by thoroughly investigat-
ing the relation of a wide range of various personal risk factors in relation to fraud victimization,
with a focus on personality traits. Socio-demographics, mental, general, and cognitive health, rou-
tine Internet activities, and prior fraud knowledge are also investigated as personal risk factors.
In the next sections, we will first discuss current issues in fraud victimization research that this
study aims to address. Then, we will discuss potential personal risk factors for fraud victimization,
summarizing previous findings and various theoretical perspectives. Finally, we will present the
research questions of the current work.
Issues in previous fraud victimization research
The use of a unified fraud taxonomy
The types of fraud that individuals may fall victim to are many (Button et al., 2009). Moreover,
fraudsters constantly evolve their methods and adapt to the latest events (e.g. COVID-19 frauds
surged quickly after the onset of the pandemic (Hoheisel et al., 2022; Kennedy et al., 2021; Ma and
McKinnon, 2022). Beals et al. (2015) recognize that this has led to a wide array of research instru-
ments based on various fraud definitions, some of which quickly became outdated. This troubles
knowledge synthesis, as comparing and merging insights from studies based on different fraud
taxonomies is difficult if not impossible.
Koning et al. 445
Therefore, Beals et al. (2015) set out to develop a general fraud taxonomy, with exhaustive and
mutually exclusive categories. This taxonomy was successfully tested with the Federal Trade
Commission’s Consumer Sentinel Network database, which contains many fraud complaint cases. By
use of this fraud taxonomy, the current study will produce insights into a distinct set of fraud types with
varying modi operandi (methods). The unified and broad nature of the fraud taxonomy ensures that
these insights are robust, futureproof, and suitable for future knowledge synthesis (Beals et al., 2015).
Measuring fraud victimization as a two-stage process: exposure and
susceptibility
Fraud victimization can be considered a two-stage process (Fan and Yu, 2022; Holtfreter et al.,
2008; Policastro and Payne, 2015). First, a part of the entire population is exposed to a fraud
attempt; then, a part of the exposed (or ‘targeted’1) population is susceptible to the fraud attempt
and becomes a victim. In other words, victimization is a product of both the likelihood of exposure
and susceptibility.2 Different factors may affect exposure and susceptibility, and the same factors
may affect exposure and susceptibility differently (Fan and Yu, 2022). Thus, improving upon pre-
vious fraud victimization studies which did not make the distinction between exposure and suscep-
tibility (e.g. DeLiema et al., 2017b), this study will measure fraud as a two-stage process. This
allows for a better understanding of the mechanisms behind fraud victimization.
The use of a representative sample
As DeLiema et al. (2017a: 7) note, a problem with fraud victimization surveys is that many rely on
non-representative samples. Some surveys, for instance, only use a sample of confirmed victims,
as identified by law enforcement or complaint agencies (e.g. Consumer Fraud Research Group,
2006; Pak and Shadel, 2011). Other studies are only done in specific subgroups of the population,
like older adults (e.g. Burnes et al., 2017; Judges et al., 2017; Xing et al., 2020; Yu et al., 2021), or,
more generally, with sampling methods that may not have been adequate for reaching a truly rep-
resentative sample (Deevy et al., 2012: 10). The latter could, for example, be because of a too-
small sample, a relatively low response rate (e.g. Centraal Bureau voor de Statistiek (CBS), 2022),
the use of self-selection sampling (Scherpenzeel, 2018), or the lack of inclusion of computer illiter-
ate participants (Eckman, 2016). Consequently, the outcomes of fraud victimization studies are
hard to compare, and sampling problems may have led to results not being true for the general
population. The current study will make use of a sampling strategy that addresses these problems.
The use of longitudinal data
As Van ’t Hoff-de Goede et al. (2023) point out, many previous victimization studies only used
data collected at one point in time. This hinders the establishment of causal relationships since
there is no temporal ordering between independent and dependent variables.3 In other words, it
cannot be said with certainty that a variable affected the risk of victimization, because victimiza-
tion may have also affected the variable; Agnew et al. (2011), for instance, show that self-control
may be reduced after victimization. The current study will therefore make use of longitudinal
data.
Get this document and AI-powered insights with a free trial of vLex and Vincent AI
Get Started for FreeUnlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations