L. Agustín, Victimisation 2003, p. 1 Forget Victimisation: Granting ...
Understanding online fraud victimisation in...
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Understanding online fraud victimisation in Australia
15th International Symposium of World Society of Victimology Dr. Monique Mann, Mr. Anthony Morgan and Dr. Marcus Smith
Australian Institute of Criminology
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Online Fraud
• Four defining elements of online fraud (Cross, Smith & Richards, 2014)
1.Via the internet 2.Dishonest invitation, request, notification or offer 3.Provide personal information or money 4.Financial or non-financial loss or impact of some kind
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$1.4 billion lost to personal fraud
1.2 million Australian victims
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Source
Jurisdiction
Sample size
Key findings
Reyns (2013)
United Kingdom 5,985
Internet banking, IM, emailing, males, older persons, higher incomes, perceive risk have increased likelihood of being a victim of identity theft.
Leukfeldt (2014)
Europe (Netherlands) 10,316
Demographic and financial characteristics did not impact the likelihood of phishing victimisation. Antivirus software had no impact on victimisation.
Reisig, Pratt & Holtfreter (2009)
United States (Florida) 573
Demographics (socially vulnerable, lower socio-economic status) influence risk perception. Individuals with higher perceived risk spend less time online and make fewer online purchases
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Source
Jurisdiction
Sample size
Key findings
Reyns (2013)
United Kingdom 5,985
Internet banking, IM, emailing, males, older persons, higher incomes, perceive risk have increased likelihood of being a victim of identity theft.
Leukfeldt (2014)
Europe (Netherlands) 10,316
Demographic and financial characteristics did not impact the likelihood of phishing victimisation. Antivirus software had no impact on victimisation.
Reisig, Pratt & Holtfreter (2009)
United States (Florida) 573
Demographics (socially vulnerable, lower socio-economic status) influence risk perception. Individuals with higher perceived risk spend less time online and make fewer online purchases
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Source
Jurisdiction
Sample size
Key findings
Reyns (2013)
United Kingdom 5,985
Internet banking, IM, emailing, males, older persons, higher incomes, perceive risk have increased likelihood of being a victim of identity theft.
Leukfeldt (2014)
Europe (Netherlands) 10,316
Demographic and financial characteristics did not impact the likelihood of phishing victimisation. Antivirus software had no impact on victimisation.
Reisig, Pratt & Holtfreter (2009)
United States (Florida) 573
Demographics (socially vulnerable, lower socio-economic status) influence risk perception. Individuals with higher perceived risk spend less time online and make fewer online purchases
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Source
Jurisdiction
Sample size
Key findings
Reyns (2013)
United Kingdom 5,985
Internet banking, IM, emailing, males, older persons, higher incomes, perceive risk have increased likelihood of being a victim of identity theft.
Leukfeldt (2014)
Europe (Netherlands) 10,316
Demographic and financial characteristics did not impact the likelihood of phishing victimisation. Antivirus software had no impact on victimisation.
Reisig, Pratt & Holtfreter (2009)
United States (Florida) 573
Demographics (socially vulnerable, lower socio-economic status) influence risk perception. Individuals with higher perceived risk spend less time online and make fewer online purchases
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Routine Activity Theory (Cohen & Felson, 1979)
Suitable target
Motivated offender Absence of a
capable guardian
ONLINE FRAUD VICTIMISATION
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Suitable target
Motivated offender Absence of a
capable guardian
ONLINE FRAUD VICTIMISATION
What is the relationship between victimisation and:
• Demographics • Online activities
• Perceived risk • Prevention behaviour
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Method and Analytic Approach
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Online fraud victimisation (n=1,786)
n %
Victim 345 19.32
Not Victim 1,441 80.68
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Category Independent Variables
Demographics Gender
Age
Education
Income
Online activities Social media use
Tablet use
Online shopping
Preventative behaviour Passwords
Security measures including anti-virus
Financial protection
Spam filters
Interact with known persons only
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Category Independent Variables
Demographics Gender
Age
Education
Income
Online activities Social media use
Tablet use
Online shopping
Preventative behaviour Passwords
Security measures including anti-virus
Financial protection
Spam filters
Interact with known persons only
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Category Independent Variables
Demographics Gender
Age
Education
Income
Online activities Social media use
Tablet use
Online shopping
Preventative behaviour Passwords
Security measures including anti-virus
Financial protection
Spam filters
Interact with known persons only
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Relationship between education and victimisation
13
19
26 28
14
18
22
29
22
9
0
5
10
15
20
25
30
35
40
45
50
Less than Year 12 orSecondary School
Year 12 or equivalent Vocational qualification Bachelor degree Postgraduate degree
%
Victim
Not victim
χ2 (4, n= 1,786) = 18.03, p.<.001, Cramer’s V= 0.1 (small effect size)
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Relationship between income and victimisation
11
25
33
17
4
11
17
24
29
16
2
12
0
5
10
15
20
25
30
35
40
45
50
$0-18,200 $18,201-37,000 $37,001-80,000 $80,001-$180,000 $180,001 and over I'd rather not say
%
Victim
Not victim
χ2 (5, n= 1,786) = 11.40, p.<.05, Cramer’s V= 0.08 (small effect size)
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Relationship between online shopping and victimisation
2
10
17 20
31
20
6
23 20
23 21
6
0
5
10
15
20
25
30
35
40
45
50
Never Less often Every 2-3 months Monthly 2-3 times a month Weekly or moreoften
%
Victim
Not victim
χ2 (5, n= 1,786) = 80.40, p.<.000, Cramer’s V= 0.21 (medium effect size)
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Relationship between risk perception and victimisation
41
59
30
70
0
10
20
30
40
50
60
70
80
90
100
Perceived risk No risk
%
VictimNot victim
χ2 (1, n=1,703) = 15.90, p.<.000, Cramer’s V= 0.1 (small effect size)
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Logistic Regression Predicting Victimisation
Predictors Odds Ratio (Ѱ) 95%CIѰ p<
Income
$80,001-$180,000 (vs $37,001-$80,000) 0.68 0.47-1.00 .05
Online shopping
Every 2-3 months (vs 2-3 times a month) 0.56 0.39-0.80 .01
Less often than 2-3 months (vs 2-3 times a month) 0.35 0.23-0.55 .001
Never (vs 2-3 times a month) 0.34 0.16-0.75 .01
Weekly or more often (vs 2-3 times a month) 1.86 1.26-2.74 .01
Risk perception (vs no risk perception) 1.40 1.07-1.81 .05
Model: χ2 (df=18, n= 1,703) = 102.34, p<.05, AUC = 0.67, Pseudo R2 = 0.06, Cox & Snell R2=0.06, Nagelkerke R2= 0.09
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Risk factors: 1. Income
• $37,001-$80,000 2. Frequent online shoppers
• Weekly or more often • 2-3 times per month • Every 2-3 months • Less often than 2-3 months • Never
3. Risk perception
R
ISK
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Key findings
• Complicated risk profile, divergences with traditional victim profile
• Analysis has gone a little further, but only explains a small amount of risk
• Need to take a more nuanced approach to understanding online fraud
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Relationship between risk perception and prevention behaviour
49
82 78
56
86 84
0102030405060708090
100
Use spam filters Use financialprotection
Interact knownpersons only
%
Risk
No Risk
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The way forward
• Examine what influences the likelihood of victimisation for other online crimes (e.g. attacks on computer system and virus), and differences for sub-categories of online fraud (e.g. romance scams versus work from home scams)
• Investigate protective factors and impact of preventative strategies
for specific sub-categories of online fraud • Examine characteristics and predictors of online repeat victims,
limited literature that examines repeat victimisation in online context