FraudFraud fraud Criminal deception involving unauthorized payments or use of financial credentials. and risk management in payments involves protecting every step of the payment process—from customer authenticationAuthentication authentication A security process used to verify the identity of the user or cardholder. May involve passwords, biometrics, OTPs (one-time passwords), or 3-D Secure. to transaction authorizationAuthorization authorization The real-time process of verifying that a payment method has sufficient funds or credit limit for a transaction. Results in an authorization code from the issuer.—to ensure that only legitimate transactionsTransactions transactions Interactions where value is exchanged for goods or services. occur. As Visa emphasizes, the fraud landscape is more sophisticated than ever, with attackers exploiting new vulnerabilities. For payment providers and merchants, effective risk management is vital to prevent financial losses, protect consumer trust, and comply with regulations.
Regulators agree: the U.S. OCC calls fraud risk management a vital component of effective payment systems risk management. Globally, fraud losses are enormous and growing. A recent study projects merchantMerchant merchant An individual or business that accepts payments in exchange for goods or services. losses from online payment fraud to exceed $362 billion by 2028. Even in secure markets, fraud remains substantial: for example, UK Finance reports £1.2 billion stolen through fraud in 2022, although firms stopped 61.5% of attempts.
These figures demonstrate why layered fraud controls are critical. In the EU, the Revised Payment Services Directive (PSD2) requires Strong Customer Authentication (SCA) for online paymentsOnline Payments online-payments Transactions conducted over the internet using card details, digital wallets, or online banking., meaning two independent factors of identification (something the customer knows, has, or is). In the U.S., while there is no blanket federal requirement, payment network rules and examiner guidelines emphasize robust authentication and real-time monitoring. Card networks like VisaVisa visa A leading global payment technology company connecting consumers, businesses, and banks. and MastercardMasterCard mastercard A global payments network enabling electronic transactions between banks, merchants, and cardholders. have implemented liability-shift rules and protocols like 3-D Secure to enforce stronger authentication.
Traditional vs. Modern Fraud Prevention
| Aspect | Traditional Approach | Modern Approach |
|---|---|---|
| Authentication | Single-factor (e.g., password, CVV) | Multi-factor (e.g., OTP, biometrics, device-based tokens) |
| Decision Logic | Static rules | AI/ML models learning from data |
| Data Usage | Basic transaction data | Behavioral, device, and contextual data |
| Adaptability | Manual rule updates | Continuous model training |
| Response Time | Batch/manual review | Real-time scoring |
| Customer Friction | Uniform checks | Risk-based, adaptive flows |
| Scope | Card-present only | Omnichannel (eCommerceeCommerce ecommerce Commercial transactions conducted electronically on the internet. Includes digital payments, shopping carts, and fraud prevention., POS, P2P, RTP, etc.) |
Modern systems integrate both rule-based engines and machine learning models, creating hybrid fraud detection frameworks. Traditional fraud filters (e.g., block list, velocity rules) are still used but enhanced by AI models trained to detect subtle and emerging fraud patterns.
Core Fraud Prevention Techniques
Two-Factor and Multi-Factor Authentication
Strong Customer Authentication under PSD2 mandates using two of the following: knowledge (e.g., password), possession (e.g., device), or inherence (e.g., fingerprint). U.S. issuers and fintechs often implement 2FA voluntarily, especially for high-risk transactions.
Device & Channel Verification
Device fingerprinting identifies and tracks devices using attributes like OS, browser, IP address, and geolocation. Tokenization replaces card numbers with secure tokens, reducing exposure in digital walletsDigital Wallets digital-wallets Applications or platforms (like Apple Pay, Google Pay) that store payment card data securely and allow users to pay digitally. and APIs. EMV chips verify card authenticity during in-person payments.
Out-of-Band Verification
High-risk transactions may trigger verification via a different channel (e.g., phone call, email). Challenge questions and push alerts allow users to confirm or flag activity.
Transaction & Behavioral Monitoring
Systems track and score transactions in real-time. Behavioral analytics profiles customer habits (time, amount, location, merchant type). Sudden deviations raise alerts and dynamically adjust risk thresholds.
Intelligence Sharing
Visa, Mastercard, and others share fraud intelligence across a global network. Consortium databases, blacklists, and real-time fraud data enhance model accuracy and proactive fraud defense.
Modern Detection Technologies
Machine Learning
ML algorithms (e.g., gradient boosting, neural nets) detect fraud by learning from millions of labeled transactions. Risk scores are assigned based on hundreds of features—transaction history, device data, network behavior, and more.
Unsupervised Learning
Anomaly detection models flag unusual patterns without needing historical labels. These help spot new fraud tactics before they become widespread.
Behavioral Biometrics
Typing cadence, swipe patterns, and touch pressure create user-specific behavioral signatures. If these deviate, the system can initiate step-up authentication or block the transaction.
Network Graphs
Fraud networks often involve linked elements (cards, devices, IPs). Graph analytics uncover fraud rings by mapping these relationships in real time.
Risk-Based Scoring
Each event is assigned a risk score. Thresholds define workflows:
- Low risk: Transaction approved
- Medium risk: Step-up (e.g., OTP)
- High risk: Transaction declinedDeclined declined A transaction that is not approved by the issuer. Reasons may include insufficient funds, suspected fraud, or incorrect card details.
This dynamic routing reduces friction while maintaining securitySecurity security Measures used to protect transaction data from fraud and cyber threats..
Real-World Examples
Visa & Mastercard
Visa Protect scans over 500 million payments per day using neural networks and real-time scoring. Mastercard uses graph-based generative AI to uncover fraud rings, doubling detection rates for compromised cards.
EMV Adoption in the U.S.
Following the 2015 EMV liability shiftLiability Shift liability-shift A risk transfer where the party not supporting EMV or 3-D Secure bears the cost of fraudulent transactions., card-present fraud decreased dramatically as merchants adopted chip-enabled terminals. This event pushed widespread migration to secure authentication methods.
PSD2 SCA in Europe
Europe’s enforcement of SCA significantly reduced unauthorized online payments. Merchants deploying 3-D Secure 2.0 with risk-based authentication report lower chargebacks and better customer experience.
FintechFintech fintech Short for financial technology, refers to tech-enabled innovation in financial services. Platforms
Companies like Stripe, PayPal, and Sift use proprietary AI systems to evaluate every transaction in real time. Sift routes risky transactions for review or challenges, and allows safe ones to proceed frictionlessly.
Banking Collaboratives
UK Finance, Nacha, and other bodies facilitate intelligence sharing and fraud reporting. Banks contribute to consortium databases and educate users about phishing, impersonation, and refund scams.
Closing Thoughts
Fraud and risk management is not a single solution—it is an evolving framework of technologies, policies, and shared intelligence. The fintech ecosystem must continue to balance security with experience through adaptive controls, AI-based decisioning, and global cooperation.
Whether it’s PSD2 in Europe or EMV in the U.S., the future of fraud prevention lies in intelligence-driven platforms that identify fraud faster, more accurately, and with less customer friction. With global fraud losses rising, effective risk management is no longer optional—it is foundational to the trust and security of the digital payments ecosystem.
For fintech professionals, staying updated on the latest machine learning advancements, regulatory shifts, and collaborative defense strategies is key to staying ahead in the arms race against fraud.