Fraud Detection Mechanisms in the MANIC Payment Network Ecosystem
The MANIC framework-comprising Merchant, Acquiring Bank, Network, Issuing Bank, and Customer-forms the backbone of credit card transactions. Each entity plays a critical role in detecting and mitigating fraud at different stages of the payment workflow. This report examines the fraud detection strategies employed by each component, leveraging advanced technologies, regulatory frameworks, and collaborative data-sharing practices to secure the ecosystem.
1 Merchant: Initial Transaction Screening and Risk Mitigation
Merchants serve as the entry point for transactions and implement pre-authorization fraud checks to filter suspicious activity before transmitting requests to acquirers. Device fingerprinting analyzes hardware and software configurations to identify anomalies, such as mismatched geolocation data or spoofed devices. Velocity checks flag unusual transaction patterns, such as rapid-fire purchases from a single IP address or device, which often indicate card-testing attacks. For high-risk transactions, merchants deploy 3D Secure (3DS), requiring customers to authenticate via one-time passwords or biometric verification, shifting liability to issuers upon successful authentication.
Point-of-sale (POS) systems also incorporate encryption and tokenization to safeguard cardholder data. For example, Clover’s POS systems encrypt transaction data end-to-end, preventing skimming attacks targeting legacy systems. Additionally, merchants monitor for force-posted fraud, where criminals use forged authorization codes to process offline transactions. By restricting weekend or holiday sales volumes and validating authorization codes, merchants reduce exposure to these schemes.
2 Acquiring Bank: Real-Time Monitoring and Merchant Profiling
Acquiring banks partner with merchants to process transactions while enforcing anti-fraud protocols. Advanced solutions like BANKiQ’s Fraud Risk Control (FRC) platform screen merchants during onboarding, analyzing business types, transaction histories, and hidden affiliations to flag high-risk entities. Post-onboarding, acquirers employ real-time transaction monitoring to detect anomalies such as sudden spikes in chargebacks or mismatched merchant category codes (MCCs).
Mastercard’s Excessive Fraud Merchant (EFM) Program exemplifies acquirer-level oversight. It calculates monthly fraud ratios by dividing fraud chargebacks by prior-month sales, imposing fines on merchants exceeding thresholds (e.g., ≥1,000 transactions and ≥$50,000 in fraud). Acquirers also leverage network-level data to identify cross-merchant fraud patterns. For instance, a surge in declines from specific card ranges may indicate coordinated card-testing attacks, prompting acquirers to block implicated IP addresses or devices.
3 Network: AI-Driven Authorization and Ecosystem-Wide Threat Intelligence
Networks like Visa and Mastercard act as central hubs for transaction routing and fraud analytics. Visa’s Advanced Authorization uses machine learning to evaluate over 500 risk attributes-including spending habits, device type, and transaction location-generating a risk score (1–99) in milliseconds. High-risk transactions trigger automatic alerts to issuers, enabling real-time declines. This system has reduced Visa’s global fraud rate to <0.1%, despite a 10x increase in transaction volume since 2005.
Visa’s Scam Disruption Practice extends beyond transactional analysis, deploying dark web surveillance and generative AI to map scam networks. By correlating phishing domains, fraudulent merchant accounts, and money mule accounts, Visa dismantles entire fraud ecosystems. In 2024, this initiative prevented $350 million in fraud, highlighting the effectiveness of proactive threat hunting. Networks also standardize security protocols, such as 3DS mandates in the EU and UK, which reduce card-not-present (CNP) fraud by requiring multi-factor authentication.
4 Issuing Bank: Behavioral Analytics and Post-Authorization Controls
Issuing banks finalize transaction approvals while safeguarding cardholder accounts. Real-time risk scoring tools, like those from Sardine.ai, analyze device handling patterns (e.g., phone tilt angles) and typing rhythms to distinguish legitimate users from fraudsters. Unusual activities, such as foreign transactions or rapid gift card purchases, trigger automatic holds and SMS alerts to cardholders.
Post-authorization, issuers review CVV mismatches and address verification service (AVS) failures to identify stolen cards. For example, a transaction approved despite an incorrect CVV may indicate account compromise, prompting the issuer to freeze the card and contact the customer. Machine learning models trained on historical fraud data further refine detection accuracy. J.P. Morgan’s fraud team, for instance, uses transaction velocity rules to block bots executing card-testing attacks, reducing false declines by 22%.
5 Customer: Behavioral Triggers and Authentication Participation
While customers do not directly implement fraud controls, their behavior influences risk assessments. Sudden deviations from typical spending patterns-such as large purchases at unfamiliar merchants-activate issuer-level flags. Customers also participate in 3DS authentication, verifying transactions via OTPs or biometrics, which reduces friendly fraud claims by confirming intent.
Educating customers on recognizing phishing attempts and securing card details remains critical. For instance, Visa’s public awareness campaigns have reduced social engineering scams by 18% in markets with high adoption of 3DS.
6 Conclusion: Collaborative Defense Across the MANIC Framework
The MANIC ecosystem’s fraud detection efficacy stems from layered defenses at each transactional node. Merchants filter early-stage risks, acquirers enforce compliance, networks deploy AI-driven analytics, issuers monitor behavior, and customers contribute through authentication. Emerging technologies like generative AI and decentralized fraud databases promise further enhancements, enabling real-time adaptation to evolving threats. However, persistent challenges-such as cross-border fraud and deepfake-enabled social engineering-demand continued innovation and global cooperation among MANIC stakeholders.