What cultural characteristics influence the spread of carding in different regions?

[Student]

Carding is a form of cyber fraud in which criminals use stolen bank card data (card number, CVV code, expiration date, and cardholder name) to conduct unauthorized transactions, purchase goods, withdraw funds, or sell data on the dark web. It is a global problem, the scale of which is growing with the rise of digitalization. According to the Hi-Tech Crime Trends 2020/2021 report by Group-IB, the global carding market grew by 116% year-on-year, reaching $1.9 billion, while the damage from card-related cybercrime in the US amounted to approximately $11 billion in 2020. Cultural factors play a key role in the spread of carding, as they influence user behavior, financial literacy, trust in technology, and social norms. Below is a detailed analysis of how cultural factors influence the spread of carding in different regions, with a focus on educational aspects to explain the mechanisms and provide insight into global differences.

1. Cultural factors influencing the spread of carding​

Cultural characteristics interact with economic, technological, and legal conditions, creating unique conditions for the spread of carding in each region. Let's examine the key cultural factors that determine a society's vulnerability or resilience to this type of fraud.

1.1. Attitude to debt and loans​

Cultural perceptions of debt and credit cards significantly influence the use of financial instruments, which is directly related to the risks of carding.

• Individualistic cultures (e.g., the US, Canada, Western Europe): In these societies, debt is perceived as a tool for achieving "success" or as part of consumer culture. Credit cards are ubiquitous (in the US, over 80% of households have at least one credit card). This creates a fertile ground for carders, as many transactions occur online, and users often store card data on e-commerce platforms. For example, in the US in 2020, approximately 40% of card fraud cases were related to online purchases.
• Collectivist and conservative cultures (e.g., India, China, the Middle East): Here, debt may be considered socially undesirable, and credit cards are less popular. For example, in India, only about 5-10% of the population actively uses credit cards, preferring debit cards or cash. This reduces the number of potential targets for carders, but does not eliminate the risks, especially with the rise of mobile payments.

Educational takeaway: In individualistic cultures, it's important to raise awareness about card data security, as high transaction volumes increase risks. In conservative cultures, the emphasis on transitioning to digital payments should be accompanied by training in secure practices.

1.2. Level of financial literacy and awareness​

Financial literacy is the ability to understand the risks associated with digital finance and take steps to protect data. Cultural factors, such as attitudes toward education or trust in financial institutions, play a key role.

• High financial literacy (e.g., Germany, Japan): In cultures where education is valued and public and private campaigns actively raise awareness of cyber risks, users are less likely to fall victim to phishing or skimming. For example, in Germany, banks are required to conduct regular customer information campaigns, which reduces fraud (carding losses in the EU account for approximately 5-7% of global losses).
• Low financial literacy (e.g., CIS countries, Africa): In regions with low education levels or distrust of banks, users are more likely to ignore fraud warnings. For example, in Russia, according to Group-IB, card fraud losses amounted to $400 million in 2011, and low awareness (especially among the elderly) remains a problem. In Nigeria, the growth of mobile payments (M-Pesa) has not been accompanied by adequate education, making the region vulnerable.

Educational conclusion: Low financial literacy is a catalyst for carding. Education should be adapted to cultural specifics: in collectivist societies, through communities and families; in individualistic societies, through personalized campaigns.

1.3. Collectivism vs. Individualism​

Cultural differences between collectivism and individualism influence the behavior of both victims and scammers.

• Collectivist societies (Asia, Latin America): In these cultures, people tend to trust close circles (family, friends), which can be used for social engineering. For example, in China, phishing attacks are often disguised as messages from "trusted" individuals. However, strict social norms and fear of "losing face" can limit the anonymity of carders, as criminals risk stigmatization within the community.
• Individualistic societies (USA, Australia): Online anonymity and less reliance on social connections make carding "safer" for scammers. At the same time, users are less likely to share data with others, reducing the risk of social media leaks.

Educational takeaway: In collectivist cultures, protecting against carding requires an emphasis on security within communities (e.g., education through religious or family institutions). In individualistic cultures, the focus is on personal responsibility and data protection.

1.4. Digital culture and access to technology​

The speed of digitalization and attitudes toward technology determine the volume of online transactions and, accordingly, the scale of carding.

• High digitalization (USA, China, South Korea): In these countries, youth cultures focused on gadgets and e-commerce are increasing the number of transactions, creating more entry points for carders. For example, in China, the growth of platforms like WeChat Pay and Alipay has led to a 20-30% annual increase in carding. However, developed infrastructure (2FA, biometrics) can offset these risks.
• Low digitalization (rural regions of India, Africa): Conservative attitudes toward technology reduce card usage, but the rise of mobile payments (e.g., UPI in India) creates new vulnerabilities. Users new to digital platforms are often unaware of security measures.

Educational conclusion: Digitalization is a double-edged sword. Educational programs must take into account the level of technological adaptation: in digital societies, the focus should be on advanced security measures, while in transitional societies, it should be on basic skills (such as phishing recognition).

1.5. Social Norms of Trust and Stigma​

Cultural norms related to trust in institutions and victim stigmatization influence crime reporting and the effectiveness of anti-carding efforts.

• Low trust in authorities (post-Soviet countries, Latin America, Africa): In these regions, people are less likely to report fraud due to a lack of trust in the police or banks. For example, in Brazil, only 30% of cybercrime victims seek help, allowing carders to operate with impunity. In Nigeria, the cultural norm of "self-reliance" and the stigma of "victim blaming" exacerbate the problem.
• High trust in institutions (Scandinavia, Japan): In these countries, users actively report fraud, and banks and police respond quickly, reducing the incidence of carding.

Educational takeaway: In cultures with low trust in authorities, alternative support channels (e.g. NGO hotlines) need to be developed and stigma reduced to encourage reporting.

2. Regional analysis: how cultural characteristics manifest themselves in practice​

To illustrate how cultural factors influence carding, we'll look at specific regions with examples and statistics.

2.1. North America (USA, Canada)​

• Cultural characteristics: Individualism, consumer culture, and high digitalization. Debt is considered the norm (the average household debt in the US is $145,000). Online shopping is part of the "American Dream" (Amazon, eBay).
• Impact on carding: The US is the largest market for carders due to the widespread use of credit cards (191 million cards in circulation in 2020). Carding costs amounted to $11 billion in 2020. Phishing and skimming are thriving due to the habit of storing card data in browsers. However, increased financial literacy (FTC campaigns) and strict laws (Fair Credit Reporting Act) allow victims to recover funds more quickly through chargebacks.
• Example: Attacks on large retailers (Target in 2013, data leak of 40 million cards) demonstrate the vulnerability due to the mass use of cards.

Educational takeaway: In the US, financial literacy campaigns should emphasize e-commerce data protection and the use of 2FA.

2.2. Europe (Western and Eastern)​

• Western Europe (Germany, France, Netherlands):
  • Cultural characteristics: Conservative attitude towards debt (debt = risk), high financial literacy, trust in banks. Culture of "transparency" and strict regulation (GDPR).
  • Impact on carding: Low carding rates (5–7% of global losses). Users are less likely to store card data online, and banks are actively implementing 3D-Secure. However, attacks on small businesses (phishing through fake payment gateways) remain a problem.
  • Example: In Germany, cybersecurity campaigns (e.g. BSI) reduced phishing incidents by 15% between 2019 and 2021.
• Eastern Europe and CIS (Russia, Ukraine):
  • Cultural characteristics: Post-Soviet mistrust of banks, tolerance for "gray" schemes, and a youth hacker subculture. Economic instability encourages carding as an "easy way to make money."
  • Impact on carding: The region is a hub for carders. Forums (Carding Mafia, ValidCC) sell card data in Russia and Ukraine. Damages in Russia in 2011 were $400 million, with losses expected to increase in the 2020s due to digitalization. Low trust in the authorities reduces reporting.
  • Example: In 2020, hackers in Russia stole data from 1 million cards through fake online shopping websites.

Educational conclusion: In Western Europe, a focus on regulation and technology (3D-Secure) is effective. In the CIS, campaigns that address mistrust and combat hacker communities are needed.

2.3. Asia (China, India, Southeast Asia)​

• China:
  • Cultural characteristics: Collectivism, "face culture" (avoidance of shame), high digitalization (WeChat, Alipay). Low trust in foreign platforms.
  • Impact on carding: The growth of mobile payments increases vulnerabilities (carding has grown by 20-30% year-on-year). Phishing through fake QR codes is popular. Strict censorship and social control make reporting difficult, as victims fear "losing face."
  • Example: In 2019, hackers stole the data of 10 million users through fake Alipay apps.
• India:
  • Cultural characteristics: Collectivism, caste system, low financial literacy in rural areas, rise of UPI (Unified Payments Interface).
  • Impact on carding: Low credit card penetration (5-10%), but the growth of digital payments (UPI processed 4.6 billion transactions in 2022) has created new risks. Phishing via SMS and phone calls is popular due to the gullibility of the rural population.
  • Example: In 2021, 1.3 million cards were compromised through fake UPI websites.
• Southeast Asia (Thailand, Vietnam):
  • Cultural characteristics: Transition to digitalization, collectivism, low trust in banks.
  • Impact on carding: The growth of e-commerce (Shopee, Lazada) is increasing attacks. In Thailand, carding damage increased by 50% from 2018 to 2022.

Educational conclusion: In Asia, security must balance tradition and digitalization. In China, the emphasis is on QR security, while in India, it's on educating rural populations.

2.4. Latin America (Brazil, Mexico)​

• Cultural characteristics: Collectivism ("familismo"), economic inequality, low trust in authorities. Carding is perceived as a means of "survival" in poor communities.
• Impact on carding: High fraud rates (damages in Brazil: $1.5 billion in 2022). Sharing cards within families increases leaks. Low reporting due to fear of censure.
• Example: In Mexico, phishing attacks via WhatsApp increased by 70% between 2020 and 2022.

Educational takeaway: Campaigns should embrace family values and offer protection through local communities.

2.5. Africa (Nigeria, South Africa)​

• Cultural characteristics: Postcolonial mistrust of institutions, victim-blame stigma, rise of mobile payments (M-Pesa).
• Impact on carding: The region is a "hot spot" (150% growth over 5 years). Nigeria is a center of "419 scams" (phishing schemes). Mobile payments create new vulnerabilities.
• Example: In 2021, hackers in South Africa stole data from 1.4 million cards through counterfeit payment terminals.

Educational takeaway: Education through mobile platforms and reducing stigma against victims are key to the fight.

3. Practical recommendations for protection​

Cultural characteristics require adapted protective measures:

1. Technological measures:
   • Use two-factor authentication (2FA) and virtual cards.
   • In collectivist cultures, avoid sharing these cards even with loved ones.
   • In digital societies, check platform security (HTTPS, 3D-Secure).
2. Educational measures:
   • In individualistic countries: emphasis on personal responsibility (cybersecurity courses).
   • In collectivist systems: learning through families, religious or local communities.
   • In regions with low trust: create independent support channels (NGOs, hotlines).
3. Culturally responsive campaigns:
   • In Asia: focus on mobile payments and QR codes.
   • In the CIS: combating the hacker subculture through youth programs.
   • In Africa: Reducing stigma through media and opinion leaders.

4. Conclusion​

Cultural factors create unique conditions for the spread of carding: individualism and digitalization increase risks in the US and Europe, while collectivism and distrust of authorities increase them in Asia, the CIS, and Africa. Understanding these factors allows us to develop effective protection measures tailored to the region. For an in-depth analysis of a specific country or statistics, please specify your request, and I will provide more detailed information!