Amazon operates the world's most sophisticated e-commerce fraud prevention ecosystem, processing trillions of dollars annually while serving over 300 million active customer accounts globally. Its defenses are multi-layered, combining proprietary machine learning, real-time behavioral analytics, tokenization, dynamic authentication, and extensive post-transaction monitoring. Following the official shutdown of new customer onboarding for Amazon Fraud Detector on November 7, 2025 (with full support ending in 2026), Amazon has fully migrated advanced fraud capabilities to Amazon SageMaker, AutoGluon, and integrated AWS services. This transition enables even more customizable, scalable, and explainable ML models tailored to Amazon's massive transaction volume.
In late 2025, direct card-not-present (CNP) carding — attempting to add and use stolen card details (CCs, fullz, dumps) for purchases — has become one of the least viable fraud vectors on Amazon. Estimated success rates for even highly optimized attempts are consistently below 20-30%, with the majority failing at card addition, checkout, or post-approval stages. IC3 data shows over 5,100 account takeover (ATO) complaints related to Amazon from January to November 2025, totaling more than $262 million in reported losses — highlighting that fraud has decisively shifted from raw card exploitation to identity-based attacks like phishing, impersonation, and ATO.
2025–2026 Outlook: Amazon's SageMaker-powered evolution, combined with network tokenization, behavioral biometrics, and cross-industry data-sharing, will further compress traditional carding viability to near-zero for most actors. Fraud will continue migrating to AI-assisted social engineering and ATO.
In late 2025, direct card-not-present (CNP) carding — attempting to add and use stolen card details (CCs, fullz, dumps) for purchases — has become one of the least viable fraud vectors on Amazon. Estimated success rates for even highly optimized attempts are consistently below 20-30%, with the majority failing at card addition, checkout, or post-approval stages. IC3 data shows over 5,100 account takeover (ATO) complaints related to Amazon from January to November 2025, totaling more than $262 million in reported losses — highlighting that fraud has decisively shifted from raw card exploitation to identity-based attacks like phishing, impersonation, and ATO.
In-Depth Breakdown of Amazon's Anti-Fraud Stack (2025 Implementation)
Amazon's defenses operate as an integrated, real-time pipeline with no single point of failure:- Machine Learning & Predictive Analytics (Post-Fraud Detector Era):
- Migration to SageMaker and AutoGluon allows deployment of custom ensemble models, including supervised, unsupervised, and graph-based learning.
- Models analyze hundreds of signals per transaction: historical purchase patterns, session velocity, item affinity, pricing anomalies, and cross-account linkages.
- Graph neural networks (GNNs) detect organized fraud rings, mule accounts, and coordinated testing.
- Explainable AI components ensure rapid iteration and regulatory compliance.
- Behavioral and Device Analytics:
- Continuous passive monitoring of session attributes: keystroke dynamics, mouse trajectories, touch patterns (mobile), scroll speed, and hesitation points.
- Device fingerprinting via probabilistic hashing (canvas, WebGL, audio context, hardware concurrency, installed fonts).
- Cross-device and cross-session learning: Even rotated fingerprints are linked via behavioral signatures.
- Tokenization and Data Minimization:
- Full card details never touch Amazon servers — handled exclusively by processors (Adyen, Stripe, others).
- Network tokenization increasingly applied, replacing PANs with device/wallet-bound tokens that are useless if intercepted.
- Dynamic Risk-Based Authentication (3DS/SCA):
- Step-up challenges (OTP, biometric verification, push notifications) triggered by real-time risk scores.
- High-risk scenarios (new device, geo-velocity mismatch, unusual order value) almost always require authentication.
- Non-VBV or "low-friction" cards are rapidly profiled and subjected to the same scrutiny.
- Velocity and Pattern Enforcement:
- Strict limits on card additions, address changes, high-value orders, and gift card reloads from new or suspicious sessions.
- Anomaly detection for "warming" patterns — small purchases followed by large ones.
- Post-Transaction Monitoring and Intervention:
- Orders can be held, delayed, or canceled retroactively if new risk signals emerge (e.g., victim report, bank flag, behavioral re-score).
- A-to-Z Guarantee investigations provide a secondary review layer, often catching late-detected fraud.
- Proactive Customer Communication:
- Mass alerts sent to over 300 million users during 2025 holiday periods warning of impersonation scams.
- Mandatory login notifications and unusual activity alerts.
Why Traditional Carding Techniques Consistently Fail on Amazon
- Card Addition Stage: Immediate declines for mismatched geo/IP, blacklisted BINs, or detected anti-detect inconsistencies.
- Checkout Stage: Dynamic 3DS challenges impossible to satisfy without victim device access.
- Post-Approval Stage: Behavioral re-scoring or victim-initiated flags lead to cancellations (common even hours/days later).
- Account Longevity: Successful small transactions often trigger permanent bans on attempted scaling.
- Data Burn Rate: "Fresh" cards lose viability quickly due to processor data-sharing.
Expanded Defense Table: Mechanisms vs. Carding Vectors (2025 Effectiveness)
| Defense Layer | Core Technologies & Policies | Primary Carding Vectors Blocked | Effectiveness Level |
|---|---|---|---|
| ML Detection (SageMaker/AutoGluon) | Custom ensembles, GNNs, behavioral scoring | Velocity, anomalies, organized rings | Extremely High |
| Device & Behavioral Fingerprinting | Passive session tracking, probabilistic hashing | Anti-detect tools, device rotation | Very High |
| Tokenization & Processor Isolation | Adyen/Stripe handling, network tokens | Raw data exposure, card testing | Extremely High |
| Dynamic 3DS/SCA | Risk-based step-up (OTP/biometrics/push) | Non-VBV/low-friction attempts | High to Very High |
| Velocity & Pattern Rules | Real-time limits on adds/orders/changes | Warming and scaling attempts | Very High |
| Post-Transaction Review | Holds, cancellations, A-to-Z investigations | Late-detected fraud | High |
| Proactive Alerts & Communication | Mass warnings, mandatory notifications | Impersonation/phishing supporting carding | Rising High |
Current Dominant Fraud Trends on Amazon (2025)
Direct CNP carding has been largely supplanted by higher-yield identity attacks:- Impersonation & Phishing: Fraudulent emails/texts claiming account issues, urgent action required (Notes sharp holiday increases).
- Account Takeovers (ATO): Credential stuffing, phishing for 2FA codes, or social engineering for recovery.
- Gift Card Draining & Reload Fraud: Targeting stored/reloaded balances via compromised accounts.
- Emerging Deepfake Threats: Early reports of voice/video spoofing for customer service bypass.
2025–2026 Outlook: Amazon's SageMaker-powered evolution, combined with network tokenization, behavioral biometrics, and cross-industry data-sharing, will further compress traditional carding viability to near-zero for most actors. Fraud will continue migrating to AI-assisted social engineering and ATO.