Abstract

Background: The growing number of older adults online potentially increases cybercrime exposure. Factors that constellate in old age are thought to be associated with greater susceptibility to cybercrime and its consequences, but there is limited research empirically testing this. Methods: We analysed responses from 35,069 participants aged 16+ in the 2019/20 Crime Survey of England and Wales (CSEW). We tested our hypotheses that, due to a higher prevalence of vulnerabilities associated with old age, older internet-users are more likely to report (1) cybercrime victimisation, (2) repeat victimisation, and (3) resulting financial loss. Results: Contrary to our hypothesis, 16-24s were most likely to report cybercrime, though as hypothesised, poor health was associated with victimisation. People aged 75+ were most likely to report repeat victimisation (OR 1.38, p=0.313), and associated financial loss (OR 4.25, p=0.037), relative to 16-24s. Participants of Black and mixed ethnicities were more likely to report victimisation than White participants. Discussion: Ethnic disparity in victimisation warrants investigation. Older adults may be under-reporting less-serious victimisation relative to younger adults, and this may mirror a lower propensity to report to the police. Considering their increasing internet use, future developments should focus on overcoming barriers to reporting of cybercrime by older adults. 

Cybercrime victimisation among older adults: a probability sample survey in England and Wales

Benjamin Havers¹ | Kartikeya Tripathi¹ | Alexandra Burton² | Sally McManus³ | Claudia Cooper^4
1Dawes Centre for Future Crime, Department of Security and Crime Science, Faculty of Engineering, University College London, London, UK
[email protected]

²Department of Behavioural Science and Health, University College London, London, UK

³Violence and Society Centre, City, University of London

⁴Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London

1. ABSTRACT

Background: The growing number of older adults online potentially increases cybercrime exposure. Factors that constellate in old age are thought to be associated with greater susceptibility to cybercrime and its consequences, but there is limited research empirically testing this. Methods: We analysed responses from 35,069 participants aged 16+ in the 2019/20 Crime Survey of England and Wales (CSEW). We tested our hypotheses that, due to a higher prevalence of vulnerabilities associated with old age, older internet-users are more likely to report (1) cybercrime victimisation, (2) repeat victimisation, and (3) resulting financial loss. Results: Contrary to our hypothesis, 16-24s were most likely to report cybercrime, though as hypothesised, poor health was associated with victimisation. People aged 75+ were most likely to report repeat victimisation (OR 1.38, p=0.313), and associated financial loss (OR 4.25, p=0.037), relative to 16-24s. Participants of Black and mixed ethnicities were more likely to report victimisation than White participants. Discussion: Ethnic disparity in victimisation warrants investigation. Older adults may be under-reporting less-serious victimisation relative to younger adults, and this may mirror a lower propensity to report to the police. Considering their increasing internet use, future developments should focus on overcoming barriers to reporting of cybercrime by older adults.

2. KEYWORDS

cybercrime elderly ethnicity fraud health reporting survey victimisation

3. INTRODUCTION

Global digitalisation has increased risks of cybercrime, with evidence that risk factors that constellate in older age groups may be associated with greater susceptibility ((Lin et al. 2019); as well as significant social, physical, emotional and financial consequences for those who experience it (e.g. (Tripathi, Robertson, and Cooper 2019). COVID-19 pandemic lockdowns caused a spike in cybercrime due to increased time spent online (Johnson and Nikolovska 2022). Though the growing number of older people with online access, expedited by the pandemic, has many positive benefits for society and individuals, it has increased exposure of this demographic group to cybercriminals (Cross 2021). This is consistent with Routine Activity Theory (RAT) (Cohen and Felson 1979) which states that crime occurs in spaces where a likely offender converges with a suitable target (in the absence of a capable guardian).

Cybercrimes include hacking through technological methods, and ‘social engineering’, where a victim is tricked into disclosing information needed to access a device, network (Peltier 2006) or programme such as a banking application, or into electronically transferring money. Social engineering is a broad term which includes cryptocurrency-related, phishing and romance fraud, and occurs on platforms including email and social media. It is inherently discriminatory, as attackers tailor their approach to intended victims’ vulnerabilities (Hadnagy 2018).

In a recent realist review, (Burton et al. 2022) developed a programme theory “explaining how, why and in what circumstances older adults may be at risk of becoming victims of financial cybercrime” (p.2). It proposes seven core victimisation risk factors: (i) limited cybersecurity skills or awareness, (ii) health vulnerabilities, (iii) memory loss, (iv) social isolation, (v) relative wealth, (vi) specific societal attitudes that precipitate shame or fear of loss of independence, and finally (vii) scam content developed by a motivated offender.

As well as being at greater risk of cybercrime victimisation, owing to perceived greater wealth and assets, older adults may be particularly attractive as a target of financial fraud online (Burton et al. 2022). At the same time, older adults who experience greater socio-economic hardships, or who are more likely to be socially isolated, for example due to bereavement, may feel more inclined to engage with fraudulent approaches that offer financial incentives (Cross 2021). Recent analysis by Age UK (Age UK 2020) indicates that over 55s in England and Wales lost over £4m to cyber fraud between April 2018 and March 2019.

The Crime Survey of England and Wales (CSEW) is a rich source of crime victimisation data that is not affected by some of the issues that limit police-recorded crime statistics, such as unwillingness to involve the police (Van Dijk 2015) and non-standardised reporting practices across forces (Tilley and Tseloni 2016). It has been suggested that for many members of the public there is a perceived lack of incentive to reporting cybercrime, as well as a lack of awareness around how to report it (McMurdie 2016). Some victims may be unaware of the crime they have experienced. Meanwhile, there is an expectation that the police and the judiciary take cybercrime reports as seriously as ‘physical’ crime (Button et al. 2022), further highlighting the benefit of independent survey data. CSEW data has been used to analyse the prevalence of specific crimes such as sexual and domestic violence ((Cooper and Obolenskaya 2021)) and all crime ((Ariel and Bland 2019), to explore who is at greater risk of experiencing domestic abuse ((ONS 2018), to understand prevention of burglary ((Tseloni et al. 2017)), reporting of hate crime ((Myers and Lantz 2020)), immigrant trust in the police ((Bradford et al. 2017)), and police activity on stop and search within Muslim communities ((Hargreaves 2018)).

CSEW-based research on cybercrime has been limited to date. Furnell and Dowling (Furnell and Dowling 2019)2019) compared CSEW data with police statistics to offer “a portrait of the landscape”, considering the challenges involved with classifying and measuring cybercrime, and its associated costs and harms. Given that the internet, and the internet of things, is now integral to daily life and could therefore facilitate an infinite number of ‘cyber-enabled’ crimes, Furnell and Dowling use the term ‘cyber-dependent’ crimes. Akdemir and Lawless (Akdemir and Lawless 2020) used CSEW data and victim interviews to test the applicability of the Lifestyle Routine Activities Theory (LRAT), an adapted form of the RAT which conceives risk of victimisation in terms of probability according to one’s overall lifestyle (Pratt and Turanovic 2016). Neither of these studies explored how frailties and comorbidities associated with old age, including cognitive, mental and physical illness or social isolation, may influence victimisation. In fact, until 2017, CSEW self-completion modules, which exist for questions on topics which the participant might feel uncomfortable discussing with an interviewer, had an upper age limit of 59 (“User Guide to Crime Statistics for England and Wales: March 2020 - Office for National Statistics” n.d.).

(Poppleton, Lymperopoulou, and Molina, n.d.)(2021) conducted the only CESW study to specifically address such vulnerabilities. They used victim and incident related risk factors and level of harm caused to divide England and Wales’ general fraud victim population into nine clusters, two of which incorporate older adults. This study demonstrates the importance of considering risk profiles for fraud (not exclusively cyber-dependent), which are more illuminating in considering how to reduce crime than prevalence statistics alone.

The current study was conceived to inform the development of tailored and targeted preventative measures for the contexts experienced by older populations. We aim to explore how reporting of cybercrime victimisation, repeat victimisation and financial impact are associated with age and other sociodemographic characteristics, and whether these relationships are influenced by economic and health-related factors and behaviours. While previous studies, many of which have reported lower rates of cybercrime and fraud risks in older adults, do not account for online usage ((Ross, Grossmann, and Schryer 2014); e.g. (van de Weijer, Leukfeldt, and Bernasco 2019) we explore cybercrime risks, across the age range, in people who have used the internet in the past year.

We test our hypothesis that among people who have used the internet in the past year, the risk of cybercrime is greater in older, compared with younger adults; and that this is explained by socioeconomic and health vulnerabilities that are more common in older age. We selected variables for this analysis to explore whether factors represented in four of Burton et al.’s (2022) core victimisation risk factors are associated with greater risks of cybercrime: health vulnerabilities, memory loss, social isolation and wealth. We also test our hypothesis that older adults who report being victims of cybercrime are more likely to report financial loss than younger demographics.

4. MATERIALS AND METHODS

1. Participants and procedures

The CSEW (formerly the British Crime Survey, BCS) is an annual national crime victimisation survey carried out by the Office for National Statistics (ONS). The survey, which uses a multistage stratified sample, is administered via face-to-face interviews with more than 35,000 adults (16 and over) across England and Wales. It seeks to be representative of adults aged 16+ living in private households in England. Participants are randomly selected from the Postcode Address File held by Royal Mail. Participants are asked whether they have been a victim of crime(s) in the past 12 months, and other personal information on topics such as housing, work and health. The current study will draw from the most recent wave of CSEW data (2019/2020). Interviews were held between April 2019 and March 2020.

2. Measures

Outcome measures

Cybercrime is defined using the ONS classification of cyber-related fraud and computer misuse, which mimics Home Office Counting Rules (Home Office 2022) for recorded crime. Interviewers ascertained whether an offence was ‘cyber-related’ by asking the participant “thinking about the incident as a whole, was the internet, any type of online activity or internet-enabled device related to any aspect of the offence?” (ONS 2019). The dichotomous primary outcome was whether participants reported being a victim of cyber-related fraud and computer misuse at all in the last 12 months. Dichotomous secondary outcomes were (a) whether the participant was a repeat victim of fraud and computer misuse; to determine this, participants were asked whether they were victimised more than once during the 12 months prior to interview; and (b) whether a participant who had experienced victimisation in the past year reported that they experienced financial loss as a result of their fraud and computer misuse victimisation. Fewer participants than those who completed the primary outcome answered the question regarding financial loss, hence the reduced sample size in Table 2.

Exposure variables

We included sociodemographic variables; and variables that reflect four of Burton et al.’s (2022) seven risk factors: health vulnerabilities, memory loss, social isolation, and wealth. We were unable to include three risk factors – societal attitudes, scam content and cybersecurity skills or awareness – as the extent to which they are covered by CSEW data is limited.

Sociodemographic/economic variables: We included age categorised in five bands: (16-24; 25-44; 45-64; 65-74; 75 and over); gender; and self-reported ONS harmonised ethnic group that was available in five groups (see Table 1 for categories for this and other variables). We also included Indices of Multiple Deprivation (IMD) to measure area deprivation. This combines information from seven domains [income deprivation; employment deprivation; education, skills and training deprivation; health deprivation and disability; crime; barriers to housing and services; and living environment deprivation]. The resulting scores are translated into ‘Lower-Layer Super Output Area’ (LSOA: small geographical areas of approximately 1500 residents) deciles (Department for Communities and Local Government, 2015) within the survey, which we converted into quintiles. We included tenure type, number of household members, hours spent away from home per weekday, and participant’s most recent occupation.

Health variables: All health variables were self-reported. The first, ‘status of health in general’, was answerable with: ‘Very good’; ‘Good’; ‘Fair’; ‘Poor; and ‘Very poor’. Participants were also asked to self-report the “presence of physical or mental health conditions or illnesses affecting the following areas”: vision and hearing (here, grouped as ‘sensory conditions’); mobility, dexterity, stamina or breathing or fatigue (grouped as ‘physical conditions’); learning or understanding or concentrating, and memory (‘cognitive conditions’); and lastly, mental health and ‘socially or behaviourally’ (‘mental conditions’).

3. Analysis

All analyses were performed using Stata 17. Where participants answered ‘don’t know’ or ‘not applicable’ or refused to answer a question, the data was treated as missing. We excluded participants who stated that they had not accessed the internet in the last year. We weighted data using the calibration weighting variable developed for the original CSEW survey design, “designed to make adjustments for known differences in response rates between different age and gender sub-groups” (CSEW 2020), and report actual numbers and weighted percentages throughout.

We first used standard summary descriptive statistics to characterise the sample (see Table 1). We then used univariate logistic regression analysis to investigate associations of the exposure variables with primary and secondary outcomes (see Table 2). In our multivariate logistic regression analysis, to test the hypothesis that risk of cybercrime victimisation and repeat victimisation would be associated with age and that this would be explained by health and social variables, we conducted two series of forward stepwise logistic regressions in which we entered variables in the following order: (1) sociodemographic and socioeconomic measures, (2) health measures.

To test our hypothesis that cybercrime victimisation in older age was more likely to be associated with financial loss, we conducted a logistic regression with experience of financial loss as the dependent variable and age group as the independent variable. For this analysis, we restricted our sample to people who had reported any cybercrime victimisation in the past 12 months; and investigated the proportion of respondents who had, and had not reported related financial loss. Those not answering this question were excluded.

5. RESULTS

1. Sample Description

Of the 36,913 participants in the survey, we excluded 738 who did not complete the primary outcome; and 1106 who reported that they had not used a computer in the past year (603 (54.52%) aged 75+, 292 (26.40%) aged 65-75, and 211 (34.99%) aged 16-64). The total sample size was therefore 35,069. Table 1 shows the sociodemographic characteristics of the sample. Victimisation was reported by 2564 (7.31%) of participants, and repeat victimisation by 455 (1.30%) of participants. 659 (25.70%) participants reporting victimisation answered the question regarding whether they had experienced financial loss as a result of their cybercrime victimisation, or not. Of these 659 participants, 268 (40.51%) answered that they had, whilst 391 (59.69%) answered that they had not.

2. Univariate Analyses

Associations with age

Risk of cybercrime victimisation reduced with age, being highest among people aged 16-24 and lowest in people aged 75+. By contrast, repeat victimisation was reported most frequently by people aged 75+, though this difference relative to the youngest age group was not statistically significant (Table 1). As hypothesised, older adults were more likely than younger adults to report financial loss as a result of their victimisation (Table 2). People aged 75 and over were significantly more likely (OR 4.25, p=0.04) than participants aged 16-24 to report financial loss.

Associations with other sociodemographic/economic exposures

Of our other exposures (see Table 1), cybercrime victimisation was associated with: being male; being of Black or mixed/multiple ethnic groups; living in less deprived areas; living in larger households; spending more time away from home per weekday; living in private rented accommodation (relative to owned accommodation); and those who reported their most recent occupation as managerial/professional. Repeat victimisation was associated with: being male; being of mixed/multiple and other ethnic groups; and living in social rented accommodation (relative to owned accommodation). Cybercrime victimisation and repeat victimisation were associated with poor general health, as well as the presence of physical, mental and cognitive conditions/illnesses.

3. Multivariate Analysis

In fully adjusted multivariate models (see Table 3), 16-24 year olds remained at most risk of victimisation. The results for the remaining sociodemographic/economic and health-related exposure variables closely reflect those found during our univariate analysis. Victimisation and repeat victimisation were associated with: Black and mixed ethnicity; male gender; greater time spent away from home per weekday; managerial/professional occupations; rented accommodation tenure; poor general health; and mental and physical conditions/illnesses.

6. DISCUSSION

Contrary to our hypothesis, reporting any cybercrime victimisation was less common with advancing age, though people aged 75+ were most likely to report repeat victimisation and financial loss. As hypothesised, worse cognitive, physical, mental and general health were associated with greater risk.

There are several possible explanations for our findings. It might be, given that cases reported by older people were more likely to involve financial loss and repeat victimisation, that they were more serious in nature and therefore represent the “tip of the iceberg”. This may be indicative of significant under-reporting of cybercrime by older people in this survey. (Burton et al. 2022) theorise based on existing literature that older adults may not disclose their victimisation due to feelings of shame, embarrassment, and fear of not being taken seriously or being victim blamed.

Our findings could also indicate that mitigating factors reduce risk of cybercrime in this population. One is time spent online. Whitty (Whitty 2019) found that “younger people were more likely to engage in routine activities that potentially expose them to cyber-frauds and older people were more likely to engage in online guardianship behaviours”. It is in line with the RAT that cybercriminals have greater opportunity to target people who are online more. Older people still use the internet least, but as there are many benefits to internet use, reducing access is not a solution to decreasing risk. The significant increased engagement of older populations in online worlds suggests that identifying and strengthening other mitigating factors in this demographic group is critical. A second likely mitigating factor is guardianship; both self-initiated strategies such as using antivirus software, and support of family and friends where possible, might decrease risk. A survey by (Branley-Bell et al. 2022) found old age to be associated with cybersecurity behaviours such as generating secure passwords and regularly updating devices. Younger users, however, were more likely to secure their devices by, for example, locking the screen – possibly because older adults actively allow access to trusted individuals.

It is notable that living in private rented accommodation, and living in larger households, is associated with greater risk. This may be a confounder in our finding that age is negatively correlated with victimisation; older people are most likely to be owner occupiers (ONS 2020) and living either alone or with their partner. Alternatively, this residential stability could be a protective factor. (Garg and Camp 2015) theorise that high residential mobility hampers guardianship in the form of social ties with neighbours. Staying safe online may be easier without frequent moves of accommodation.

Our results show that men are more likely to report victimisation and repeat victimisation than women. A plausible explanation is that men, who have been found to take more risks than women generally (Hudgens and Fatkin 1985), may also engage in riskier behaviour or activities online, leaving them more vulnerable to malicious actors.

This study also found that Black and mixed/multiple ethnicities were significantly more likely to be victims than participants of White ethnicity. Research on the drivers behind ethnic disparities in crime victimisation in the UK and abroad is extremely limited. Future research might explore differing patterns and types of internet use, and systemic disadvantages, for example linguistic barriers to safe cyber navigation.

Our findings suggest a complex relationship with socioeconomic status, as those in professional and managerial occupations are more likely to experience cybercrime, as are those who are unemployed. A plausible explanation, consistent with LRAT, could be that professional and managerial occupations involve greater internet usage, whilst the unemployed are more vulnerable because they have fewer resources to protect themselves from crime.

Poor general and mental health were associated with cybercrime victimisation, which may in part be a product of victimisation rather than a cause or predictor. There may be scope for incorporating cybersecurity-related assistance/education into health and social care services, as well as improved multiagency collaboration and information sharing with police. Given that victimisation was associated with poor physical and cognitive health; software professionals might consider how online platforms and their security features and offerings can be made more inclusive.

Limitations

Our conceptualisation of victimisation – here considered to be the ‘receipt’ of an attack regardless of consequences - is legitimate given that it aims to capture reporting. However, others might view it exclusively as someone who has lost or suffered as a result of the attack, with all other cases seen as a failed attempt only. Secondly, in our repeat victimisation definition we were only able to include cases of victimisation from within the last year. Finally, perhaps the most significant limitation of this research is that it is based on pre-COVID-19 data only, and habits, behaviours and threats may have changed significantly as a result of the pandemic (Cross 2021).

Conclusion

Older people may be under-reporting less serious victimisation (that does not involve repeat offences or financial loss), relative to younger adults when asked in surveys, and this may mirror a lower propensity to report crime to the police. Tailored future developments in platform design and multi-agency collaboration and information sharing should focus on overcoming barriers to reporting of cybercrime by older adults.

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TABLE 1 Sociodemographic characteristics of the sample and univariate associations with cybercrime victimisation and repeat victimisation

TABLE 2 Financial loss summary statistics and univariate analysis

TABLE 3 Sociodemographic characteristics of the sample and multivariate associations with cybercrime victimisation and repeat victimisation