Biometric payments are a transaction authentication method based on unique physiological or behavioral characteristics of a person, such as facial recognition, fingerprints, iris scanning, or even voice analysis. These technologies are integrated into payment systems to verify a user's identity without the need for a PIN or a physical card. For example, systems like Apple Pay or Alipay allow users to pay for purchases simply by looking into their device's camera.
Carding is a type of financial fraud in which criminals steal credit or debit card information (numbers, CVV, expiration dates) and use it for unauthorized purchases, often online, or for cash withdrawals. Traditionally, carding relies on data breaches, phishing, or skimming (theft of data from magnetic stripes). Advances in biometrics promise to revolutionize this field, making traditional methods less effective but also creating new challenges. For educational purposes, we'll break this down step by step, using current trends as of 2026.
Ultimately, biometrics make carding less profitable, as it requires fraudsters to move beyond simple data interception to more sophisticated attacks.
Vulnerabilities table for clarity:
The evolution of payments from cash to biometrics shows that fraud is adapting: from skimming to deepfakes.
Overall, biometrics will reduce traditional carding, but will make fraud more sophisticated, highlighting the need for continuous cybersecurity education.
Carding is a type of financial fraud in which criminals steal credit or debit card information (numbers, CVV, expiration dates) and use it for unauthorized purchases, often online, or for cash withdrawals. Traditionally, carding relies on data breaches, phishing, or skimming (theft of data from magnetic stripes). Advances in biometrics promise to revolutionize this field, making traditional methods less effective but also creating new challenges. For educational purposes, we'll break this down step by step, using current trends as of 2026.
Positive aspects: How biometrics reduce the risks of carding
Biometric systems enhance payment security by replacing easily forged elements (like card numbers) with "something you are" — unique biological markers that are difficult to replicate without physical access. This is consistent with the multi-factor authentication (MFA) model, where biometrics are combined with other factors, such as device or geolocation.- Reducing fraud losses: According to research, the implementation of biometrics in payments has already led to a 42% reduction in fraud at global financial institutions by 2025. By 2026, biometric payments are expected to save billions of dollars by preventing the use of stolen data without biometric verification. For example, biometric cards (with an integrated fingerprint scanner) allow banks to control the user experience, minimizing the risks associated with mobile platforms.
- Real-time verification: In systems with facial recognition, transactions are confirmed instantly, making real-time carding difficult. Visa notes that by 2025, biometrics will integrate with "invisible payments," where authentication occurs in the background, reducing vulnerabilities to data theft.
- AI integration for anomaly detection: AI analyzes behavioral patterns (such as facial movement speed) to identify fraudulent attempts. This is particularly effective against mass carding, where carders test thousands of stolen cards.
Ultimately, biometrics make carding less profitable, as it requires fraudsters to move beyond simple data interception to more sophisticated attacks.
Vulnerabilities in Biometric Systems and New Vectors for Carding
Despite its advantages, biometrics are not without their weaknesses. Unlike passwords, biometric data cannot be changed once compromised, creating long-term risks. Fraudsters are constantly evolving, adapting to new technologies.- Spoofing and counterfeiting: Spoofing attacks involve using masks, 3D-printed facial models, or deepfake videos to fool facial recognition systems. Research shows that early systems were vulnerable to photos or videos, while modern systems are vulnerable to AI-generated deepfakes, which can fool up to 20% of systems. In payments, this allows carders to bypass authentication using stolen biometric data.
- Database Hacking: Biometric data is stored in centralized databases that can be hacked. If hackers gain access, they can use the data for identity theft, including carding. In 2025, the threat of biometric theft, using AI to create fake identities, is growing.
- Social engineering and injection attacks: Fraudsters can trick users into providing biometric data (for example, through phishing apps) or exploit software vulnerabilities, such as injection attacks on facial authentication.
- Bias and false positives: Systems can be biased by race, gender, or age, leading to errors and potential exploits.
Vulnerabilities table for clarity:
| Vulnerability type | Description | An example of the impact on carding |
|---|---|---|
| Spoofing | Forging biometrics using deepfakes or masks | Bypassing authentication to use stolen cards |
| Data hacking | Hacking Biometric Template Databases | Long-term use of data for multiple frauds |
| Injection attacks | Injecting malicious code into the system | Automatic approval of transactions without actual verification |
| Social engineering | Deceiving the user to gain access | Biometric theft through fake apps |
The evolution of carding in the era of biometrics
Carding is evolving from simple data theft to sophisticated attacks on biometric systems. Historically, biometrics have evolved from fingerprints in the 19th century to modern AI systems. By 2025, biometric cards and mobile payments will become standard, but fraudsters are migrating to:- Hybrid attacks: Combining carding with biometric fraud, such as using deepfakes for real-time payments.
- Focus on niche methods: Attacks on systems without biometrics or on weak links, such as voice deepfakes in voice payments.
- The rise of AI fraud: Online fraud will reach $200 billion by 2026, with biometrics a key barrier, but also a target.
The evolution of payments from cash to biometrics shows that fraud is adapting: from skimming to deepfakes.
Examples of biometric fraud in 2025-2026
- Deepfake attacks on verification: By 2025, 1 in 20 verification attempts will be fake, growing 21% year-on-year. Examples include the use of AI to create fake identities in banking.
- Real-time payment fraud: Fraudsters use biometric counterfeits for instant transfers, as in the case of voiceprints.
- Mobile fraud: Biometric contactless payments will account for 68% of transactions in 2026, but are vulnerable to attacks on mobile devices.
- Synthetic ID fraud: Creating fake biometric profiles for credit card fraud.
Future trends and recommendations
By 2026, biometrics will become dominant in payments, but will require improvements: anti-spoofing algorithms, decentralized data storage, and combination with AI. For consumers: use MFA, keep devices updated. For businesses: invest in liveness detection.Overall, biometrics will reduce traditional carding, but will make fraud more sophisticated, highlighting the need for continuous cybersecurity education.
