Traditional payment fraud relied on manual data entry, phishing scams, and brute-force attacks. Today’s fraudsters are smarter, faster, and more scalable thanks to AI and automation.
AI-powered fraud refers to the use of artificial intelligence, machine learning, and automation by bad actors to bypass security controls, mimic legitimate behavior, and orchestrate fraud at scale. These techniques allow fraudsters to adapt quickly, test multiple attack vectors, and exploit even the smallest weaknesses in payment systems.
Key AI-Powered Fraud Techniques
1. Synthetic Identity Fraud
Fraudsters create entirely new, fictitious identities using a blend of real and fabricated information. AI helps generate realistic personal data (names, DOBs, addresses) and test combinations to see which ones pass initial verification checks.
Why it’s dangerous: These synthetic identities can open bank accounts, acquire cards, and transact like legitimate users, often undetected for months.
How it works:
- AI generates plausible identity data using public records or leaked data sets.
- Fraudsters train models to predict which identity traits pass KYC.
- Over time they build transaction histories to increase trustworthiness.
2. Deepfake Attacks for KYC Spoofing
Using AI-generated videos and images, fraudsters can spoof biometric identity checks, bypassing onboarding and account recovery procedures.
Why it’s dangerous: Video KYC and selfie verification are increasingly used in digital onboarding, especially by fintech apps and banks.
How it works:
- AI generates deepfake images from stolen photos or public profiles.
- Fraudsters feed these into KYC systems to impersonate real users.
- Voice deepfakes can also bypass IVR systems or phone-based authentication.
3. Automated Card Testing
AI bots automatically test stolen card numbers against e-commerce and POS systems to find valid combinations.
Why it’s dangerous: These low-dollar, high-frequency attacks are hard to detect. If left unchecked, they can drain merchant accounts or flood systems with chargebacks.
How it works:
- Bots rapidly test combinations of card number, expiry date, CVV, and ZIP code.
- AI adapts patterns based on success/failure feedback.
- If one merchant blocks it, bots shift to another in seconds.
4. Adversarial ML Attacks
Fraudsters analyze the behavior of anti-fraud models and train AI to behave in ways that fool the model into thinking they’re legitimate.
Why it’s dangerous: Machine learning models are the backbone of fraud detection. Adversarial attacks can blind them.
How it works:
- AI agents simulate legitimate customer behavior during checkout.
- Transactions are spaced and structured to avoid triggering rules.
- Fraud occurs only after a high-trust “profile” is established.
Steps Solutions Providers Can Take to Mitigate Risk
Fighting AI with AI is only part of the solution. POS solution providers and merchants must adopt a multi-layered security posture that integrates technology, policy, and process.
- Upgrade to AI-Based Fraud Detection Systems
If you’re using static rules or legacy fraud detection, you’re falling behind. AI is now essential for:
- Real-time anomaly detection (spikes in velocity, location mismatches)
- Behavioral biometrics (how someone types, moves, or swipes)
- Predictive modeling (flagging likely fraud before it happens)
Tools to explore:
- Predictive modeling (flagging likely fraud before it happens)
- Biometric verification tools and transaction-level decisioning tools from third party providers.
- Custom ML models using platforms like AWS Fraud Detector or Azure ML.
- Talk to your provider to see what’s available to you today and what’s on their roadmap for the near-future.
- Adopt Dynamic, Risk-Based Authentication
Instead of forcing PIN or OTP on every transaction, use AI to trigger stronger authentication only when risk indicators are high.
- Evaluate solutions like IP reputation, device fingerprinting, location anomalies.
- Use adaptive CVM (cardholder verification methods) like biometrics or CDCVM.
- Consider whether the added cost of a service like EMV 3-D Secure (2.2+) for eCommerce and in-app fraud mitigation is justified for your use-case(s).
- Monitor for BIN Attacks and Card Testing
Payment platforms should throttle suspicious low-value authorizations and block velocity attacks. Again, check with your payment technology provider to determine what’s in place today.
- Rate-limit failed transaction attempts by IP or device.
- Require CAPTCHA or anti-bot protection on checkout where applicable.
- Monitor for patterns of repeated small-amount transactions that fall outside of a merchant’s standard transaction profile.
- Secure Your APIs
Many AI-powered attacks exploit open or weakly protected APIs in the POS integration layer.
- Implement strong API authentication (OAuth2, mutual TLS).
- Rate-limit API calls per client/device.
- Validate client app integrity using attestation services (e.g., Android SafetyNet or Apple DeviceCheck).
- Implement Device Trust & Certificate Pinning
If you’re deploying SoftPOS or cloud-based terminals, validate the integrity of the mobile device.
- Enforce device compliance before accepting sensitive transactions.
- Use root/jailbreak detection and real-time telemetry.
- Bind transactions to a trusted device identity (e.g., via X.509 certs).
- Invest in AI Threat Intelligence and Training
The simplest measure is to ensure that your support staff and merchants understand how AI fraud works.
- Subscribe to threat intel feeds that monitor AI-driven fraud rings.
- Run table-top exercises for synthetic ID attacks or deepfake onboarding attempts.
- Educate POS users on what to watch for (e.g., suspicious refund behavior, bot-like customer patterns).
AI fraud is quickly adapting and quietly reshaping the threat landscape in payments. From synthetic identities to automated testing and adversarial modeling, attackers are growing smarter and faster every day.
Good news is that the payments industry has access to countermeasures, but deploying them requires a strategic mindset, modern tools, and commitment from every layer of the ecosystem.
Whether you’re a POS software provider, an acquirer, or a merchant: you don’t just need better fraud tools… you need smarter ones.
