Association of Financial Literacy and Risk Preference With Fraud Exposure and Victimization Among Middle-Aged and Older Adults in China

Abstract

Fraud jeopardizes older people’s physical, mental, and economic well-being, and financial literacy and risk preference have been linked to the risk of fraud experience. Given the inconsistent evidence, we investigated whether financial literacy and risk preferences were associated with fraud exposure and victimization among middle-aged and older adults. We analyzed data from the 2015 China Household Finance Survey, including 22,121 participants aged 50 and older. Poisson regression models were conducted to determine the association of financial literacy and risk preference with fraud experience. We found that financial literacy and risk preference were positively associated with fraud exposure and victimization. The prevalence of fraud victimization among middle-aged and older adults with the highest financial literacy and risk preference levels was 46.5% and 45.8% higher than among those with the lowest levels, respectively. These findings suggested that basic financial knowledge is insufficient to protect older people from fraud exposure and victimization.

Keywords

fraud exposure fraud victimization financial literacy risk preferences China Household Finance Survey

What this paper adds

Fraud jeopardizes older people’s physical, mental, and financial health, and it has been hypothesized that financial literacy and risk preference are associated with the risk of fraud experience; however, previous findings have been inconsistent.

We analyzed data of 22,121 participants aged 50 and older and discovered that financial literacy and risk preference were positively related to fraud exposure and victimization, especially for messaging app and phishing fraud exposure.

The prevalence of fraud victimization was 46.5% and 45.8% higher among older Chinese with the highest levels of financial literacy and risk preference, respectively, than those with the lowest levels.

Applications of study findings

The study results suggested that basic financial knowledge is insufficient to protect older people from fraud exposure and victimization.

Although the positive relationship between financial literacy and fraud exposure reflects that financial knowledge may be helpful in identifying fraud, it also increases the risk of being enticed by benefits and falling into the trap of fraud.

To reduce fraud victimization, policies can raise citizens' awareness, particularly for those with basic financial literacy and high risk-taking preference.

Introduction

Fraud is a critical social and public health issue that harms the country’s economic development and jeopardizes individuals' well-being (Brenner et al., 2020; Burnes et al., 2017). Fraud can be defined as the intentional misrepresentation, concealment, or omission of facts about promised goods, services, benefits, and consequences that are nonexistent, unnecessary, never intended to be provided, or deliberately distorted for financial gain (Beals et al., 2015; Titus et al., 1995). Victims of fraud and scams usually experience negative emotions such as anger, stress, sadness, shame, embarrassment, guilt, and fear (Kieffer, 2015; Wilson, 2021). Furthermore, fraud victims are frequently traumatized, resulting in severe physical and mental health consequences such as depression, generalized anxiety disorder, physical discomfort, and the use of tranquilizers. (Button et al., 2014; Lewis et al., 2014).

Many studies have identified older adults as a high-risk group for fraud (Burnes et al., 2017; DeLiema, 2018; Holtfreter et al., 2014). Researchers have considered that the psychological characteristics of aging lead people to be more vulnerable to fraud (Kircanski et al., 2018; Lichtenberg et al., 2013, 2016). Aside from financial losses, fraud will harm older victims' mental, physical, and economic well-being, resulting in irreversible psychological burdens with devastating health consequences (Lichtenberg et al., 2016; Nguyen et al., 2021; Shao et al., 2019).

There are only a few theories that provide causal explanations for the fraud experiences of older people (Jackson & Hafemeister, 2013). Fraud is a complicated phenomenon, and no individual determinant can uniquely predict fraud victimization (DeLiema, 2018). Previous works on the issue of fraud victimization and scams among the older population have concentrated on criminology, psychology, and physiology. Our understanding of the prevalence of financial fraud or exploitation among older adults is primarily limited to the economic abuse. Little is known about the factors contributing to financial fraud (DeLiema et al., 2017, 2020).

Nevertheless, financial literacy is widely considered a protective factor in avoiding fraud (Judges et al., 2017; Karp, 2012). Inadequate financial knowledge may lead to ineffective information screening and vulnerability to fraud, whereas increased financial literacy protects against financial and investment fraud (Gamble et al., 2013; Lusardi, 2012). Conversely, some studies have found a positive relationship between financial literacy and fraud victimization (DeLiema, 2018; DeLiema et al., 2020; Drew & Cross, 2016). This association could be due to overconfidence among those with higher levels of financial knowledge (National Association of Securities Dealers, 2006). In addition to financial literacy, psychological traits such as financial risk preference were hypothesized to be related to older adults' fraud experience, as fraud victims are more likely to be risk-takers (Gamble et al., 2014; Norris et al., 2019; Shao et al., 2019). A recent study has suggested that further research should investigate both risk and protective factors for financial fraud to better understand the various psychological mechanisms underlying fraud throughout the life course (Gunderson & ten Brinke, 2022).

Most research on the prevalence and determinants of fraud has been conducted in western countries, whereas few studies have been conducted in Asian societies. However, by 2030, a quarter of Asia’s population is estimated to be over 60 (Khan, 2019; Leeson, 2018). Particularly, China has the world’s largest population of older adults, accounting for nearly a quarter of the global population. The Chinese population over 60 is predicted to increase from 17.4% in 2020 to 30% in 2040 (Mao et al., 2020). Notably, a national sample survey in China revealed that the prevalence of encountering financial frauds was 22.8% for those aged 50–59 and 26.1% for those aged 60 and above. Moreover, those over 50 suffer higher monetary loss for financial fraud victimization than those under 50. For example, those over 50 show a higher percentage of losses over 100,000 RMB (approximately 15,600 USD) than younger age groups (Dong & Gan, 2018). As a result, the vulnerability of older people to fraud is a critical social and public health issue in China, as fraud can cause financial, physical, and psychological harm to older adults (Burnes et al., 2017; Shao et al., 2019; Xing et al., 2020). Unfortunately, only a few studies have examined fraud victimization among older Chinese and those have relied on small and non-representative samples (Li et al., 2016; Shao et al., 2019; Xing et al., 2020). Although recent studies have estimated the national prevalence and risk factors for consumer financial fraud in China (Fan & Yu, 2021a, 2021b), these works did not conduct age-specific analyses to investigate the risk factors for fraud experience in middle-aged and older people.

Additionally, most studies only focus on fraud victimization among older people but do not investigate their experiences of encountering fraud events. Studies have found that older victims of fraud frequently underestimate their victimization (Deevy & Beals, 2013; Deevy et al., 2012), whereas fraudsters may target middle-aged and older adults to defraud their savings or pensions (Li et al., 2016). Consequently, the vulnerability of middle-aged and older adults to fraud exposure needs to be examined. Furthermore, a review on older adults’ vulnerability to fraud has indicated that distinguishing between different types of fraud is critical, but research has rarely distinguished various forms of fraud (Shao et al., 2019). The relationship between demographic characteristics and fraud risk may vary depending on the fraud channels. For example, certain types of financial fraud (e.g., telemarketing, investment, and lottery scams) are disproportionately prevalent among the older population (Deevy et al., 2012; Shadel & Pak, 2007).

Given the inconsistent evidence, it is debatable whether financial literacy protects the older population from fraud. Therefore, this study aimed to investigate the association of financial literacy and risk preference with fraud exposure and victimization among middle-aged and older adults using a representative survey data from China.

Methods

Data Source and Study Participants

The data were obtained from the 2015 China Household Finance Survey (CHFS), which included a nationally representative sample of households from 29 provinces, 351 counties, 1396 village committees, and 37,289 households. The CHFS is a nationwide biennial household survey conducted by the Survey and Research Center for China Household Finance at Southwestern University of Finance and Economics in Chengdu, China (Gan et al., 2014). The respondent was selected as the person who knew the most about the household’s financial status (i.e., the head of the household or breadwinner) for each household. The questionnaire included respondents’ sociodemographic characteristics, household assets, insurance, expenditures, and fraud experience in the preceding year. Following the age criteria of previous studies on fraud victimization among older population (Burnes et al., 2017; DeLiema et al., 2017, 2020), we restricted the participants to those aged 50 years or older. A total of 22,121 participants were analyzed in this study.

Measures

Fraud Experience

This study investigated two dimensions of fraud experience: fraud exposure and fraud victimization. Fraud exposure was measured by the question: “Over the past year, which of the following fraud types has your household encountered?” The response categories included (1) telephone fraud, (2) text message fraud, (3) messaging app fraud (e.g., QQ, WeChat), (4) phishing fraud, (5) acquaintance fraud or face-to-face scam (e.g., pyramid or improper commodity trading), and (6) any other form of fraud. If respondents answered yes to any of the above items, they were defined as having fraud exposure in the preceding year. This study also investigated fraud exposure through specific channels by the above response categories. For fraud victimization, respondents were asked if they had suffered losses due to any fraud. This study defined fraud victimization if the respondent answered yes to this question.

Financial Literacy

We assessed financial literacy by the widely used Big Three scale (Lusardi & Mitchell, 2011, 2014), which is a standard method in economics and the Health and Retirement Study (HRS) fraud research (DeLiema et al., 2020; Hastings et al., 2013). The Big Three financial literacy scale measure respondents’ knowledge about interest compound, inflation, and risk diversification by three questions: (1) Given a 4% interest rate, how much would you have in total after 1 year if you have 100 yuan deposited? (2) With an interest rate of 5% and an inflation rate of 3%, the stuff you buy with the money you have saved in the bank for 1 year is? (3) Which one do you think is riskier, stock or fund? (Lusardi, 2019). We created the financial literacy score by aggregated the number of correct answers (from 0 to 3) for each respondent. In the regression models, we used the zero-score group as the reference category.

Risk Preference

We assessed risk preference using a well-established and validated measure, which is a standard approach used in the Survey of Consumer Finances and has been applied in the study for the older population (Banks et al., 2020; Bonsang & Dohmen, 2015). The question is, “if you had money to invest, which of the statements comes closest to the level of financial risk you are willing to take when making investments?” and the response items included (1) take substantial financial risks expecting to earn substantial returns, (2) take above-average financial risks expecting to earn above-average returns; (3) take average financial risks expecting to earn average returns; (4) take below-average financial risks expecting to earn below-average returns; (5) unwilling to take any financial risk; and (6) having no idea. We categorized the risk attitude into low (unwilling to take any risk or take below-average risk), moderate (take average risk), and high (take above-average or substantial risks) risk preference. In the regression model, we used low-risk preference as the reference category.

Covariates

The control variables were sociodemographic characteristics, including sex, age group (50 to 54, 55 to 64, 65 years and above), marital status (married, unmarried, divorced/separated, widowed), educational attainment (elementary school or less, junior high school, high school, university or above), household disposable income level (divided by 100,000 RMB, approximately 15,600 USD), and debt (natural logged). We also control for participants’ level of happiness by the question: “in general, how happy do you feel?” This question was rated on a 5-point Likert scale (1–5) from extremely unhappy to extremely happy, and we considered it as a continuous variable. Additionally, the area-related covariates included urbanization level (urban, suburban, and rural area) and geographic area in China (East, Middle, and West region).

Data analysis

To investigate the association of financial literacy and risk preference with fraud exposure and victimization, we conducted multiple Poisson regression models with the robust variance to estimate the prevalence ratios (PRs) while controlling for all covariates. We first modeled financial literacy and risk preference as dummy variables and examined their relationship with fraud experience separately. To check robustness, we also analyzed financial literacy score (ranges from 0 to 3) and risk preference level (low, moderate, and high were coded as 1 to 3, excluding those who answered “no idea”) as continuous variables in the Poisson regression models. Stata statistical package version 17.0 was used to analyze the data. A p-value of less than 0.05 was considered statistically significant. All statistical analyses were two-tailed.

Results

Participant Characteristics

A total of 22,121 study participants were included in the analysis, including 16,808 men (76.0%) and 5313 women (24.0%) over the age of 50 (Table 1). The mean age was 62.8 years (SD = 9.3 years). The majority of participants were married (85.2%), and most had an elementary school education (40%), with only a small proportion had a university degree (3.8%). In terms of financial literacy, nearly half of the study participants answered all three questions incorrectly, while 32.1%, 16%, and 4% correctly answered one, two, and three questions, respectively. In terms of financial risk preference, 70.2% of participants reported a low-risk preference, whereas only a minority indicated a high-risk preference (6.0%).

Table 1.

Sociodemographic characteristics and fraud experience of participants.

Variable	n	%
Total	22,121	100.0
Gender
Male	16,808	76.0
Female	5313	24.0
Age (Mean, SD)	62.8	9.3
Age group
50–54 years old	5098	23.1
55–64 years old	8578	38.8
65 and above	8437	38.2
Marital status
Married	18,831	85.2
Unmarried	261	1.2
Divorced/separated	467	2.1
Widowed	2549	11.5
Education level
Below elementary school	8959	40.6
Junior high school	6911	31.3
High school	5386	24.4
University or above	837	3.8
Financial literacy
Zero correct answer (scored 0)	10,618	48.0
One correct answer (scored 1)	7099	32.1
Two correct answers (scored 2)	3530	16.0
Three correct answers (scored 3)	874	4.0
Risk preference
Low	15,523	70.2
Moderate	2426	11.0
High	1337	6.0
Having no idea	2835	12.8
Urban/rural
Urban	14,294	64.6
Rural	7827	35.4
Region
East	11,093	50.2
Central	6006	27.2
West	5022	22.7
Fraud experience
Fraud exposure
All types	12,248	55.4
Telephone fraud	10,879	49.2
Text message fraud	8694	39.3
Messaging app fraud	960	4.3
Phishing sites fraud	318	1.4
Acquaintances fraud	1644	7.4
Fraud victimization	794	3.6

Fraud Exposure and Victimization

In general, more than half of the study participants have encountered any form of fraud in the preceding year (55.4%). Most of these fraud exposures were telephone fraud (49.2%), followed by text message fraud (39.3%). A relatively smaller percentage of participants experienced messaging app fraud (4.3%) and phishing sites fraud (1.4%). Furthermore, 7.4% of the study participants have experienced acquaintance scams or face-to-face fraud. Most notably, 794 of the study participants (3.6%) suffered financial losses due to any form of fraud in the preceding year.

Association of Financial Literacy with Fraud Exposure and Victimization

The Poisson regression models showed that the higher the financial literacy, the higher the prevalence of fraud exposure and fraud victimization (Table 2). In the overall fraud exposure (Model 1), those who correctly answered all three financial literacy questions showed a 30.9% higher prevalence of encountering fraud compared to those who scored zero on the financial literacy scale (prevalence ratio, PR = 1.309; p < 0.001). In telephone and text message fraud exposures (Models 2 and 3), the prevalence for those who scored three on the financial literacy scale was 1.30 times to 1.46 times for those who scored zero (p < 0.001). Notably, in the text message and phishing fraud exposures (Models 4 and 5), the prevalence for scored three was as high as 6.0 times and 8.4 times for those who scored three on the Big Three scale (p < 0.001). The same PR for acquaintance fraud exposure was 1.36 (p = 0.009) (Model 6). Finally, the prevalence of fraud victimization was 46.5% higher (PR = 1.465; p = 0.023) for those who scored three in financial literacy than those who scored zero (Model 7). Most notably, those who correctly answered one financial literacy question already showed a significantly higher prevalence of fraud experience than those who answered all wrong. The higher the financial literacy score, the higher the prevalence of fraud experience. There were gradient associations between financial literacy and fraud exposure and fraud victimization.

Table 2.

Association of financial literacy with fraud exposure and victimization.

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6		Model 7
	Any type of fraud exposure		Telephone fraud exposure		Text message fraud exposure		Messaging App fraud exposure		Phishing fraud exposure		Acquaintance fraud exposure		Fraud victimization
Variable	PR	p-Value	PR	p-Value	PR	p-Value	PR	p-Value	PR	p-Value	PR	p-Value	PR	p-Value
Financial literacy
Scored 0	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Scored 1	1.224	<0.001	1.222	<0.001	1.325	<0.001	3.148	<0.001	3.523	<0.001	1.272	<0.001	1.296	0.002
Scored 2	1.300	<0.001	1.309	<0.001	1.426	<0.001	4.293	<0.001	5.485	<0.001	1.317	<0.001	1.248	0.039
Scored 3	1.309	<0.001	1.304	<0.001	1.464	<0.001	6.046	<0.001	8.387	<0.001	1.360	0.009	1.465	0.023
Gender
Male	1.060	<0.001	1.072	<0.001	1.010	0.598	0.996	0.957	0.878	0.345	1.105	0.083	1.060	0.503
Age group
50 to 54	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
55 to 64	1.038	0.014	1.069	<0.001	0.980	0.306	0.866	0.049	0.893	0.378	1.228	0.003	1.141	0.187
65 and above	1.018	0.275	1.049	0.010	0.824	<0.001	0.573	<0.001	0.514	<0.001	1.615	<0.001	1.601	<0.001
Marital status
Married	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Unmarried	0.738	<0.001	0.681	<0.001	0.632	<0.001	0.734	0.446	0.386	0.330	1.214	0.359	1.039	0.908
Divorced/separated	1.029	0.384	0.974	0.517	1.012	0.799	1.206	0.261	1.083	0.807	1.568	0.001	1.883	0.001
Widowed	0.926	<0.001	0.906	<0.001	0.917	0.007	0.922	0.537	0.832	0.453	1.022	0.780	0.865	0.236
Education
Elementary school	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Junior high school	1.198	<0.001	1.262	<0.001	1.294	<0.001	0.956	0.644	0.896	0.527	0.938	0.303	0.777	0.006
High school	1.283	<0.001	1.363	<0.001	1.449	<0.001	1.469	<0.001	1.390	0.051	1.021	0.758	0.927	0.445
University or above	1.424	<0.001	1.538	<0.001	1.594	<0.001	2.208	<0.001	1.935	0.003	1.070	0.579	1.088	0.625
Income (per 100,000)	1.013	<0.001	1.014	<0.001	1.020	<0.001	1.034	<0.001	1.037	<0.001	1.022	0.013	1.023	0.097
Debt (natural logged)	1.000	0.791	0.999	0.105	1.001	0.191	1.019	<0.001	1.020	0.002	1.004	0.168	1.019	<0.001
Happiness	0.991	0.190	0.996	0.601	0.979	0.028	0.985	0.705	1.026	0.742	0.932	0.013	0.921	0.056
Rural area	0.699	<0.001	0.661	<0.001	0.672	<0.001	0.327	<0.001	0.299	<0.001	0.761	<0.001	0.834	0.028
Region
East	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Central	0.924	<0.001	0.867	<0.001	0.963	0.057	0.796	0.005	0.914	0.512	1.317	<0.001	1.213	0.024
West	1.031	0.039	1.014	0.407	1.078	<0.001	0.914	0.275	0.855	0.306	1.500	<0.001	1.367	<0.001
Observations	22,039		22,039		22,039		22,039		22,039		22,039		22,039
Pseudo R-square	0.0284		0.0353		0.0401		0.1191		0.1105		0.0146		0.0143

Note. PR = Prevalence ratio; Ref = Reference category.

Association of Financial Risk Preference with Fraud Exposure and Victimization

Table 3 shows that the higher the financial risk preference, the higher the prevalence of fraud exposure. Across all types of fraud exposure (Models 1–6), high-risk preference participants were 1.07–2.78 times more likely to encounter fraud events than those with low-risk preference. Those with a moderate level of risk preference were 1.05–2.30 times more likely to encounter fraud events than low-risk preference participants. In contrast, those who answered having no idea to the risk preference question were less likely to experience fraud events. In fraud victimization, the prevalence was significantly higher (PR = 1.458; p = 0.003) for high-risk preference participants than those with low-risk preference (Model 7). Notably, those with a moderate level of risk preference already showed higher prevalence of fraud victimization than low-risk preference participants. There were also a gradient association between financial risk preference and fraud victimization.

Table 3.

Association of risk preference with fraud exposure and victimization.

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6		Model 7
	Any type of fraud exposure		Telephone fraud exposure		Text message fraud exposure		Messaging App fraud exposure		Phishing fraud exposure		Acquaintance fraud exposure		Fraud victimization
Variable	PR	p-Value	PR	p-Value	PR	p-Value	PR	p-Value	PR	pValue	PR	p-Value	PR	p-Value
Risk preference
Low	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Fair	1.066	<0.001	1.046	0.019	1.105	<0.001	2.298	<0.001	1.963	<0.001	1.120	0.133	1.278	0.021
High	1.076	<0.001	1.066	0.008	1.132	<0.001	2.592	<0.001	2.778	<0.001	1.333	0.001	1.458	0.003
No idea	0.790	<0.001	0.757	<0.001	0.724	<0.001	0.494	<0.001	0.441	0.012	0.937	0.390	0.965	0.749
Gender
Male	1.066	<0.001	1.079	<0.001	1.018	0.352	1.025	0.738	0.900	0.444	1.111	0.068	1.068	0.453
Age group
50 to 54	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
55 to 64	1.037	0.015	1.067	<0.001	0.981	0.321	0.900	0.148	0.914	0.486	1.233	0.003	1.154	0.154
65 and above	1.017	0.293	1.048	0.012	0.825	<0.001	0.626	<0.001	0.551	<0.001	1.619	<0.001	1.626	<0.001
Marital status
Married	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Unmarried	0.735	<0.001	0.679	<0.001	0.628	<0.001	0.674	0.340	0.339	0.260	1.197	0.396	1.026	0.939
Divorced/separated	1.026	0.444	0.972	0.473	1.006	0.897	1.159	0.384	1.033	0.922	1.546	0.001	1.849	0.001
Widowed	0.919	<0.001	0.900	<0.001	0.907	0.002	0.873	0.300	0.786	0.333	1.009	0.908	0.854	0.196
Education
Elementary school	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Junior high school	1.226	<0.001	1.290	<0.001	1.338	<0.001	1.148	0.154	1.095	0.599	0.976	0.691	0.809	0.021
High school	1.340	<0.001	1.422	<0.001	1.538	<0.001	1.867	<0.001	1.839	<0.001	1.081	0.235	0.973	0.782
University or above	1.511	<0.001	1.631	<0.001	1.726	<0.001	2.908	<0.001	2.763	<0.001	1.146	0.251	1.149	0.413
Income (per 100,000)	1.014	<0.001	1.015	<0.001	1.021	<0.001	1.031	<0.001	1.036	<0.001	1.022	0.011	1.021	0.125
Debt (natural logged)	1.000	0.912	0.999	0.083	1.001	0.280	1.017	<0.001	1.018	0.006	1.004	0.202	1.018	<0.001
Happiness	0.994	0.377	0.998	0.835	0.984	0.086	1.010	0.800	1.058	0.460	0.938	0.022	0.926	0.074
Rural area	0.679	<0.001	0.643	<0.001	0.644	<0.001	0.272	<0.001	0.242	<0.001	0.732	<0.001	0.803	0.007
Region
East	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Central	0.920	<0.001	0.862	<0.001	0.957	0.029	0.803	0.006	0.910	0.490	1.312	<0.001	1.214	0.023
West	1.023	0.117	1.007	0.674	1.068	0.001	0.878	0.115	0.821	0.198	1.483	<0.001	1.348	0.001
Observations	22,039		22,039		22,039		22,039		22,039		22,039		22,039
Pseudo R-square	0.0268		0.0342		0.0377		0.1097		0.0940		0.0137		0.0143

Note. PR = Prevalence ratio; Ref = Reference category.

Robustness Check Analysis

We also used continuous financial literacy and risk preference variables in the Poisson models as a sensitivity analysis, and the results showed similar patterns. The higher financial literacy and risk preference was associated with higher the prevalence of fraud exposure and victimization. The PRs were between 1.12 and 1.89 for financial literacy (Table S1), and between 1.04 and 1.71 for financial risk preference (Table S2).

Discussion

Main Findings

This study found that more than half of the Chinese middle-aged and older adults had encountered fraud in the preceding year. When the fraud experience was decomposed by type, half had encountered telephone fraud, while about four in ten had encountered text message fraud. On the other hand, messaging app, phishing website, and acquaintance fraud were less common. The prevalence of fraud victimization among middle-aged and older people was 3.6%. Furthermore, this study found that the higher the level of financial literacy or financial risk preference, the higher the prevalence of fraud exposure and fraud victimization. Notably, financial literacy and financial risk preference were more strongly related to fraud exposure in messaging apps and phishing websites than to other types of fraud exposure.

Comparison with Previous Studies

The results of the present study are comparable to previous studies. According to a US study, 9.1% of those aged 55–64, 7.3% of those aged 65–74, and 6.5% of those over the age of 75 are victims of consumer fraud each year in the United States (Anderson, 2013). People over the age of 50 make up 35% of the US population, but they account for 57% of all fraud victims. Another study found that people between the ages of 50–64 are most likely to report harm from major fraud (American Association of Retired Persons, 1999). A study by the American Association of Retired Persons (Ledbetter & Manager, 2003) found that only 27% of known fraud victims (losing at least $1000) admitted that they were harmed (Jackson & Hafemeister, 2013).

Similarly, based on data from the Crime Survey of England and Wales (National Bureau of Statistics, 2020), it is estimated that 7.3% of people aged 55–64 are victims of fraud (Office for National Statistics, 2020). A meta-analysis study showed that among cognitively intact, community-dwelling older adults, the overall prevalence across the 12 included studies for financial fraud and scams among older adults was 5.6%. Notably, the authors concluded that the prevalence of fraud victimization among older people is likely to be underestimated (Burnes et al., 2017). Literature has indicated that financial fraud victims often underreport their victimization to authorities or may not recognize that they have been victimized or are unaware that they have been defrauded (Deevy & Beals, 2013; Deevy et al., 2012). Also, the self-reported fraud victimization rates in the questionnaire survey could vary according to the context and interact with demographics. In particular, older women tend to underreport victimization in the crime context (Beals et al., 2017).

Previous research on the relationship between financial literacy and financial fraud has been inconclusive. A common perspective is that financial literacy protects against financial fraud and exploitation (Wood & Lichtenberg, 2017). Inadequate financial literacy may lead to ineffective information screening and susceptibility to fraud, and increased financial literacy is a protective factor against financial and investment fraud (Gamble et al., 2013; Lusardi, 2012). For example, research conducted by the Rush University Medical Center used community-based voluntary cohorts to examine the correlation of susceptibility to scams with health and financial literacy among older adults without dementia (James et al., 2014; Yu et al., 2021, 2022). Scam susceptibility is measured by five-item self-report questions assessing perceptions of con-artists and phone call answering behaviors (James et al., 2014). These studies found that low health and financial literacy is associated with susceptibility to scams and thus recommend improving older people’s financial and health literacy to prevent scam victimization (James et al., 2014; Yu et al., 2021, 2022). Nonetheless, these studies indicate that scam susceptibility is not equivalent to fraud victimization; future research can examine the association using actual scam experiences (James et al., 2014).

Conversely, studies have also shown that higher levels of financial literacy is associated with a higher risk of fraud victimization (Pak & Shadel, 2011), possibly as a result of overconfidence among those with higher financial literacy (National Association of Securities Dealers, 2006). Victims of investment fraud have higher basic financial literacy compared to non-victims (Drew & Cross, 2016). A study used the 2016 Health and Retirement Study (HRS) data with 1268 survey participants over 50 years of age randomly selected. The HRS module also includes the “Big Three” financial knowledge questions developed by Lusardi and Mitchell (2014). The study concluded that older people who are more financially literate and educated are not necessarily immune to fraud victimization (DeLiema et al., 2020). This study found a positive association of the level of financial literacy with the risk of fraud victimization and fraud exposure, particularly in the messaging app and phishing website fraud. These findings support the idea that basic financial literacy is not enough to protect people from vulnerability to fraud.

In the Chinese context, gullibility, financial risk-taking behaviors, psychological vulnerability, and social isolation were important causes for fraud victimization in older adults (Shao et al., 2019). In addition, agreeableness is related to lower fraud vulnerability, while social loneliness is a salient risk factor for vulnerability to fraud (Xing et al., 2020). The present study found that participants with risk-seeking preferences or over 65 were at higher risk of fraud victimization, which can help identify vulnerable groups and design interventions to protect citizens from fraud victimization. For example, a study found that risk preferences are linked to investor behaviors, and a simple education program can reduce the proportion of naive investors and improve investor awareness, allowing them to make better decisions and reducing the firms' incentive to commit financial fraud (Gui et al., 2017).

Limitations

This study has some limitations. First, due to the cross-sectional nature of our study design, we were unable to establish a causal relationship between the variables. However, we believe that financial literacy and risk preference are developed over time in the middle-aged and older adult population, and the fraud questions were based on the experience in the preceding year, which may indicate a temporal relationship. Second, the fraud questions in this study were designed to inquire about the household status, whereas demographic characteristics and financial literacy were the characteristics of the participants. We assumed that the participants' responses to the fraud questions were based on their own experiences. Our assumption was supported by the number of respondents who reported personal reasons for their fraud losses, which was close to the number of respondents who reported fraud victimization in their households. Finally, this study distinguished between various fraud channels but not the characteristics of fraud. Future research could further distinguish financial fraud from investment fraud and intimidation fraud.

Conclusion

In summary, this study found that more than half of the Chinese middle-aged and older adults had encountered fraud in the preceding year, primarily via telephone and text message fraud. Higher levels of financial literacy and financial risk preference were associated with a higher prevalence of fraud exposure and fraud victimization, and the relationships were gradient. In particular, higher financial literacy and risk-taking reference were crucial risk factors for messaging app and phishing fraud exposure. Future research can further elucidate the underlying mechanisms of these associations. To reduce fraud victimization, governments should raise citizens' awareness of fraud, particularly among those with basic financial literacy and high risk-taking preference. Policymakers can target middle-aged and older adults to increase their understanding of common fraud schemes. It can be pointed out that financially literate people are also vulnerable to fraud. Therefore, the public should utilize anti-fraud hotlines when in doubt to avoid overconfidence or overlooking risks. In future financial education programs, the content of financial fraud prevention should also be emphasized.

Supplemental Material

Supplemental Material - Association of Financial Literacy and Risk Preference With Fraud Exposure and Victimization Among Middle-Aged and Older Adults in China

Supplemental Material for Association of Financial Literacy and Risk Preference With Fraud Exposure and Victimization Among Middle-Aged and Older Adults in China in China by Shih-Jen Yu, Chun-Tung Kuo, and Yung-Ching Tseng in Journal of Applied Gerontology.

Footnotes

Acknowledgments

We thank the Survey and Research Center for China Household Finance at Southwestern University of Finance and Economics for the data distribution.

Author contributions

All authors significantly contributed to the manuscript. S.-J. Y. and C.-T. K. designed the study. S.-J. Y. and C.-T. K. analyzed the data and wrote the manuscript. C.-T. K. and Y.-C. T. contributed to interpreting results, critically revised the manuscript, and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

This study used de-identified secondary data publicly available from the Survey and Research Center for China Household Finance. Ethical approval was not required.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Chun-Tung Kuo

Supplemental Material

Supplemental material for this article is available online.

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