How is the payment system ecosystem structured in the context of combating carding? (The roles of issuing banks, acquirers and payment systems

[Student]

An Introduction to Carding and the Role of the Payment Ecosystem in Combating It​

Carding is a form of financial fraud in which criminals use stolen payment card data (card number, CVV, expiration date, cardholder name) to conduct unauthorized transactions. This is especially common in online environments where the card is not physically present (card-not-present, or CNP, fraud). Global losses from carding are estimated to exceed billions of dollars annually, and these losses are growing due to the development of dark web markets where stolen data is sold. Education is key: understanding the ecosystem helps businesses, consumers, and security professionals prevent risks rather than facilitate them.

The payment ecosystem is built on the principles of distributed responsibility, where each participant—the issuing bank, the acquiring bank, the payment network, and the merchant — contributes to security. It is not a linear chain, but a network of intersecting control mechanisms, governed by standards such as PCI DSS (Payment Card Industry Data Security Standard), EMV, and PSD2 in Europe. The ecosystem is evolving: from simple checks in the 1990s to AI-driven systems today that analyze billions of transactions in real time. In the fight against carding, the emphasis is on preventative measures: pattern detection, authentication, and threat intelligence sharing.

For clarity, I'll expand the table from the previous answer by adding examples, technologies, and educational insights. Then I'll describe the transaction process and key strategies.

Expanded roles for participants in the ecosystem and anti-carding efforts​

Participant	Overall role in the payment ecosystem	Role in the Fight Against Carding: Detailed Measures and Examples	Educational insights
Issuing Bank	Issues cards to clients in partnership with networks (Visa, Mastercard). Authorizes transactions, checking balances, limits, and authenticity. Pays the acquirer the amount minus interchange fees. Manages cardholder accounts.	Uses AI and machine learning for real-time monitoring: anomaly detection (e.g., unnotified purchases in another country). Blocks suspicious transactions, requiring additional verification (SMS-OTP or biometrics). Initiates chargebacks in case of fraud, compensating victims. Exchanges data on stolen cards through global databases (e.g., Visa Account Updater). Example: If a carder tests a card with small amounts, the issuer may block the card after 3-5 attempts.	Issuers are the "first barrier" for consumers. They invest in fraud scoring models (for example, FICO's Falcon), which assess risk based on 100+ parameters (geolocation, device, history). Educational: Consumers should monitor SMS notifications and use virtual cards for online purchases to minimize risks.
Acquiring Bank	Provides merchants with accounts for accepting payments. Processes merchant requests and routes them through networks. Transfers funds to the merchant after settlement, retaining fees.	Monitors merchants for high chargeback rates (a chargeback ratio of >1% is a red flag). Uses tools to detect "card testing" (multiple small transactions). Accounts of risky merchants can be suspended (for example, in high-risk industries like gambling). Ensures PCI DSS compliance. Example: An acquirer analyzes IP addresses and devices to identify bots used by carders.	Acquirers are the "guardians" of businesses. They evaluate merchants during onboarding (KYC/AML checks). Educational: Merchants should choose acquirers with strong fraud tools to avoid fines (up to $500,000 for a PCI breach). This teaches the balance between convenience and security.
Payment system (Payment Network)	Intermediary: routes authorizations and settlements between the issuer and acquirer. Sets rules and collects fees. Ensures global compatibility (for example, VisaNet processes 65,000 transactions per second).	Provides security standards: 3D Secure 2.0 for multi-factor authentication (biometrics, device fingerprinting). Tokenization (replacing card data with tokens). Global monitoring and exchange of fraud intelligence (e.g., Mastercard's Decision Intelligence). Example: Networks detect "velocity checks"—when a carder tries a card on many websites quickly.	Networks are the "coordinators" of the ecosystem. They develop technologies like EMV 3DS, reducing CNP fraud by 80% in some regions. Educational: This illustrates how collaboration (e.g., EMVCo) enhances security; students can explore how AI in networks predicts fraud with 95%+ accuracy.
Merchant	Accepts payments through gateways (e.g., Stripe, PayPal). Stores and processes data in compliance with rules. Receives funds after settlements.	Implements front-end security: CVV/AVS verification, CAPTCHA, rate limiting. Uses third-party tools (e.g., Signifyd for fraud scoring). Monitors orders for indicators (multiple IP addresses, address mismatches). In the event of fraud, suffers losses from chargebacks. Example: A merchant can delay delivery for high-risk orders and check them manually.	Merchants are the front line. They balance UX and security (overly strict checks scare off customers). Educational: Case study: Amazon uses ML for detection, reducing false positives; this teaches a data-driven approach to risk.

Transaction process with a focus on anti-carding measures (step by step)​

1. Initiation: The cardholder (or carder) enters data on the merchant's website. The merchant verifies the basic data (CVV, AVS) and sends a request to the acquirer.
2. Routing: The acquirer routes the payment through the payment network to the issuer. The network uses tokenization to prevent the data from being stored in plain text.
3. Authorization: The issuer checks the balance and fraud score. If the risk is high (abnormal pattern), it requires 3DS: the user confirms via app/biometrics. If rejected, the transaction is stopped.
4. Settlement: If approved, funds are transferred. Post-transaction monitoring: all parties monitor chargebacks (up to 120 days).
5. Fraud detection: AI analyzes device ID, IP, and behavior (for example, carders use VPNs). If fraud occurs, the issuer blocks the card, and the network notifies others.

Key strategies and technologies to combat carding​

• Authentication: 3D Secure 2.0 - freakless (no password if low risk).
• Tokenization and encryption: Data is replaced with tokens that are useless to carders.
• AI/ML: Pattern detection (card testing, account takeover). Example: Systems like Stripe Radar analyze 1,000+ signals.
• Data sharing: Consortiums like the MRC (Merchant Risk Council) share threat intelligence.
• Regulations: PCI DSS requires audits; violations result in fines.

Conclusion and educational recommendations​

An ecosystem is a dynamic system where collaboration is key to success: one weak link (for example, an unprotected merchant) threatens everyone. Carding is evolving (bots, AI-generated fakes), but so are measures: by 2025, the growth of biometrics and zero-trust models is expected. For training, I recommend resources like MRC webinars or Coursera courses on cybersecurity in payments. This knowledge helps you ethically protect systems, not exploit them. If you need clarification, please ask!