What authentication technologies can completely eliminate carding by 2030?

[Student]

For educational purposes, I will examine in detail authentication technologies that could significantly reduce card fraud by 2030, explaining their mechanisms, advantages, limitations, and implementation prospects. I will also discuss why completely eliminating card fraud is difficult and what additional measures are needed. My answer will be structured to ensure clarity and depth.

What is carding and why is it difficult to fix?​

Carding is a type of fraud in which criminals use stolen credit or debit card information to conduct unauthorized transactions. This information can be obtained through phishing, skimming, database leaks, the dark web, or malware. The main problem is that carding relies not only on technological vulnerabilities but also on human error (such as social engineering). Even the most advanced authentication systems can be bypassed if the user is deceived or does not follow security recommendations. However, the technologies described below can significantly complicate the fraudsters' task.

Key Authentication Technologies to Combat Carding​

1. Biometric authentication​

Description: Biometric systems use a person's unique physical or behavioral characteristics to verify their identity. Examples include:

• Physical biometrics: fingerprints, facial recognition (2D and 3D), iris scanning, palm vein recognition.
• Behavioral biometrics: analysis of gait, typing patterns, mouse or touchscreen movements.

How it works: Biometric data is collected by a device (e.g., a smartphone or POS terminal) and compared to a stored template. Modern standards, such as FIDO (Fast Identity Online), ensure local storage of biometric data (on the device, not on a server), minimizing the risk of leakage. For payment transactions, biometrics can be used as a second authentication factor (e.g., Face ID for Apple Pay).

How it helps against carding:

• It is not enough for an attacker to have the card number, CVV, and owner's name; they also need physical access to biometric data.
• Anti-spoofing technologies (such as face detection using IR cameras) make it difficult to use fake fingerprints or photographs.
• Behavioral biometrics can detect anomalies even if the fraudster is using a stolen device.

Outlook for 2030:

• Mass adoption: Most smartphones and payment terminals already support biometrics, and by 2030 it will become the standard for all payment systems.
• Improving accuracy: Machine learning algorithms will make biometrics more reliable by reducing the False Acceptance Rate (FAR) and False Rejection Rate (FRR).
• Integration with other systems: Biometrics will be combined with tokenization and MFA to create multi-layered security.
• Example: Systems like Apple Pay, where biometrics (Face ID/Touch ID) confirm a tokenized transaction, will become ubiquitous.

Limitations:

• Spoofing attacks: Fake fingerprints, 3D masks, or synthetic voices can bypass weak systems. Advanced anti-spoofing technologies are required.
• Privacy: Users are wary of storing biometric data despite on-premises solutions.
• Availability: Not all devices (especially budget ones) support high-quality biometrics.
• Ethical issues: In some countries, biometrics can be used for surveillance, which causes resistance from users.

Educational aspect: Biometrics is based on statistical data analysis. For example, facial recognition uses deep learning algorithms (neural networks) to match facial keypoints to a template. Students interested in this field should study machine learning, cryptography, and FIDO standards.

2. Tokenization and one-time codes​

Description: Tokenization replaces sensitive card data (PAN - Primary Account Number) with a unique digital token that is useless outside of a specific transaction or device. One-time codes (OTPs) are temporary passwords sent to the user to confirm a transaction.

How it works:

• Tokenization: When using, for example, Google Pay, card data is replaced with a token generated by the payment system (Visa, Mastercard). The token is linked to a specific device and merchant.
• One-time codes: The bank sends an OTP to the registered phone number or email, which the user enters to confirm the transaction.

How it helps against carding:

• Tokens are useless to fraudsters because they do not contain card details and are limited by time, device, or merchant.
• OTP requires access to the user's device, which makes attacks more difficult.
• Standards such as EMV 3D-Secure use tokenization and OTP to improve the security of online transactions.

Outlook for 2030:

• Global standardization: Tokenization will become mandatory for all payment systems, including small banks and payment gateways.
• Blockchain Tokens: Using decentralized blockchain-based tokens for even greater security.
• Simplified OTP: Instead of entering codes, users will be able to confirm transactions via push notifications or biometrics.
• Example: Visa Token Service technology is already used by millions of merchants and its adoption is set to grow.

Limitations:

• Social engineering: Phishing attacks can trick the user into revealing the OTP.
• Infrastructure dependence: Tokenization requires support from banks, merchants, and payment systems.
• Data Interception: If a user's device is compromised, tokens can be stolen before they can be used.

Educational aspect: Tokenization relies on cryptography and security protocols such as AES (Advanced Encryption Standard). Students will benefit from learning the basics of cryptography, payment system architecture (e.g., PCI DSS), and EMVCo standards.

3. Multi-factor authentication (MFA) using AI​

Description: MFA requires several factors to verify identity:

• Knowledge: What the user knows (password, PIN).
• Possession: Something the user has (smartphone, token).
• Biometrics: Identity of the user (fingerprint, face). AI analyzes the transaction context (location, device, time, behavior) to identify anomalies.

Mechanism of operation:

• The user enters a password, confirms the transaction via biometrics and/or receives an OTP.
• The AI system checks whether the transaction matches typical user behavior (for example, a purchase in an unusual location or on a suspicious device).
• Example: A bank blocks a transaction if a user from Moscow suddenly tries to buy something in Brazil.

How it helps against carding:

• Even if a fraudster gets hold of the card details, they won't be able to complete all the MFA levels.
• AI identifies suspicious transactions in real time, reducing the risk of unauthorized transactions.
• Adaptive authentication (Risk-Based Authentication) reduces the burden on the user by requiring MFA only when the risk is high.

Outlook for 2030:

• Next-generation AI: Algorithms will become more accurate thanks to big data and improved machine learning models.
• Contextual analysis: AI will take into account more factors (for example, data from IoT devices such as smartwatches).
• Seamless integration: MFA will be transparent to the user in most cases (e.g. automatic confirmation via behavior).
• Example: Systems similar to Google Advanced Protection will be integrated into payment platforms.

Limitations:

• False positives: AI may mistakenly block legitimate transactions.
• Data dependence: Requires access to large amounts of user data to operate accurately, raising privacy concerns.
• Resource intensity: AI systems require significant computing resources.

Educational aspect: AI in the MFA utilizes machine learning algorithms such as neural networks, decision trees, and time series analysis. Students should explore the fundamentals of ML, statistics, and anomaly detection systems.

4. Blockchain and decentralized identity (SSI)​

Description: Decentralized identity (Self-Sovereign Identity, SSI) allows users to control their digital identities through the blockchain. The user stores their data in a digital wallet and provides only the necessary information (such as proof of transaction authorization) without revealing full details.

How it works:

• The user receives a digital identity (Verifiable Credential) from a bank or other trusted authority.
• Transactions are confirmed through cryptographic signatures recorded in the blockchain.
• The merchant receives only the confirmation token, not the card details.

How it helps against carding:

• Eliminates the need to transmit card numbers or other sensitive data.
• Blockchain ensures immutability and transparency of transactions.
• It is difficult for a fraudster to counterfeit a digital ID, since it is verified through a distributed network.

Outlook for 2030:

• Global standards: W3C Verifiable Credentials and DID (Decentralized Identifiers) will become the basis for payment systems.
• Integration with banks: Large payment systems (Visa, Mastercard) are already experimenting with blockchain, for example, for cross-border payments.
• Scaling: New blockchain protocols (e.g. Ethereum 2.0, Polkadot) will solve scalability and speed issues.
• Example: Pilot projects such as uPort or Sovrin can become the basis for mass payment solutions.

Limitations:

• Implementation complexity: Requires global infrastructure and agreement between banks, merchants and regulators.
• User Experience: Managing digital wallets can be challenging for untrained users.
• Regulatory Barriers: Blockchain raises concerns among regulators due to its anonymity and lack of central control.

Educational aspect: Blockchain is based on cryptography (SHA-256, ECDSA algorithms) and distributed systems. Students will benefit from learning the basics of blockchain, smart contracts, and W3C standards.

5. Quantum-resistant cryptographic algorithms​

Description: Quantum computers can crack traditional encryption algorithms (RSA, ECC) using algorithms such as Shor's algorithm. Quantum-resistant algorithms (Post-Quantum Cryptography, PQC) are being developed to protect data in the future.

How they work:

• Algorithms based on mathematical problems that are resistant to quantum attacks (e.g. lattice cryptography, coding schemes) are used.
• They are used to encrypt data of cards, tokens and transactions.

How it helps against carding:

• Protects data even in the event of the emergence of powerful quantum computers that can crack current encryption systems.
• Ensures long-term security of payment system infrastructure.

Outlook for 2030:

• NIST Standards: The US National Institute of Standards and Technology is finalizing PQC algorithms (e.g., CRYSTALS-Kyber, CRYSTALS-Dilithium).
• Implementation in finance: Banks will begin the transition to quantum-resistant protocols to secure transactions.
• Example: Visa and Mastercard are already exploring PQC to protect their systems.

Limitations:

• Transition complexity: The entire infrastructure (servers, terminals, devices) must be updated.
• Performance: PQC algorithms can be slower and require more resources.
• Uncertainty: Quantum computers have not yet reached a level that threatens current cryptography, slowing adoption.

Educational aspect: PQC is based on complex mathematics (lattices, codes, hash functions). Students should study cryptography, number theory, and quantum computing.

6. Hardware-based security devices (HSM and TPM)​

Description: Hardware Security Modules (HSMs) and Trusted Platform Modules (TPMs) are physical devices for securely storing keys and performing cryptographic operations.

How they work:

• HSM/TPM stores encryption keys and biometric templates in a secure environment isolated from the main processor.
• Transactions are signed within the module, preventing access to keys even if the device is compromised.

How it helps against carding:

• Even if a fraudster gains access to the device, they will not be able to extract keys or card data.
• Protects against attacks at the operating system level (e.g. rootkits).

Outlook for 2030:

• Mass adoption: All smartphones, POS terminals and IoT devices will be equipped with HSM or TPM.
• Biometrics Integration: HSM will be used to store biometric data.
• Example: Chips like Apple's Secure Enclave will become standard across all devices.

Limitations:

• Cost: HSM/TPM are expensive for small businesses and budget devices.
• Physical Attacks: Rare but possible equipment-level attacks (e.g. laser attacks).
• Compatibility: Standardization between manufacturers is required.

Educational aspect: HSM/TPM utilize cryptographic primitives and secure hardware architectures. Students benefit from studying microelectronics, cryptography, and security standards (e.g., FIPS 140-2).

Why is it difficult to completely eliminate carding by 2030?​

1. Human factor: Social engineering (phishing, scam calls) remains a weak link. Even the most secure systems are vulnerable if the user is deceived.
2. Fraudsters adapt quickly: Fraudsters quickly adapt to new technologies by finding vulnerabilities or using alternative methods (for example, stealing accounts instead of card data).
3. Global heterogeneity: Not all regions and companies will be able to implement advanced technologies by 2030 due to financial, technical or regulatory constraints.
4. Economic incentive: Carding is a profitable business, and fraudsters will continue to find ways to circumvent the systems.

Additional measures to reduce carding​

1. User education: Digital literacy programs can help reduce vulnerability to phishing and other attacks.
2. Regulatory requirements: Laws requiring the use of MFA, tokenization, and biometrics for all transactions.
3. International Cooperation: Combating darknet markets and data leaks requires coordination across borders.
4. Technology Integration: The combination of biometrics, tokenization, AI, and blockchain creates multi-layered security that is harder to bypass.

Forecast to 2030​

Carding will not disappear completely by 2030, but its scale could be significantly reduced thanks to:

• Universal tokenization: All transactions will use tokens instead of real card data.
• Biometric standard: Most devices will support biometrics as a mandatory authentication factor.
• Real-time AI: Fraud detection systems will become faster and more accurate.
• Blockchain Identification: SSI will become an alternative to traditional payment data.
• Quantum Preparation: Financial infrastructure will begin the transition to PQC.

Achieving these goals will require collaboration between tech companies, banks, regulators, and users. Completely eliminating carding is only possible by eliminating the human factor, which is unrealistic in the coming years.

Suggestions for study​

For students interested in anti-carding:

1. Cryptography: Learn AES, RSA, ECDSA, and post-quantum algorithms (courses on Coursera, books like "Cryptography and Network Security" by William Stallings).
2. Machine Learning: Understanding Algorithms for Anomaly Detection (TensorFlow, PyTorch, edX courses).
3. Blockchain: Fundamentals of Distributed Systems and Smart Contracts (Udemy courses, Ethereum documentation).
4. Payment systems: EMVCo, PCI DSS and FIDO standards (official websites, technical specifications).
5. Cybersecurity: Fundamentals of protection against phishing, skimming, and device attacks (CompTIA Security+, CEH certifications).

If you'd like to dive deeper into a specific technology or get some code/script examples, let me know and I'll provide more details!