By Maxim Spivakovsky

The payment fraud landscape is rapidly evolving, with fraudulent card transactions projected to total more than $400 billion worldwide over the next decade. To protect against this rising threat, banks are investing heavily in fraud prevention and recovery efforts—to the tune of 23 percent of their operational costs, on average. However, as banks fortify their defenses, they must also maintain intuitive and frictionless experience for customers–who increasingly demand both security and convenience. 

As banks seek to strike this balance, AI and machine learning have emerged as powerful tools both increasing fraud detection rates and reducing false positives. Advanced authentication techniques, such as behavioral biometrics, are further revolutionizing fraud prevention.

Read on to learn more about actionable strategies your bank can use to build intelligent fraud controls that align with modern customer expectations.

Risk-based authentication: adjusting friction in real-time

One of the most effective fraud prevention strategies is risk-based authentication (RBA), which dynamically adjusts authentication requirements based on real-time risk signals. RBA ensures that customers only experience friction when necessary, balancing security with a seamless user experience. Here’s how RBA works: 

• Evaluating risk signals: Each transaction is assessed based on criteria such as location, device attributes, network signals, and behavioral patterns. AI-driven systems continuously analyze these factors, adjusting authentication steps accordingly.

• Dynamic response: For lower-risk transactions, minimal friction methods like push notifications, one-time passcodes, or biometric verification are used. High-risk scenarios trigger stronger authentication steps, such as multi-factor authentication (MFA) or identity verification challenges.

• Continuous monitoring: RBA continuously learns from customer behavior, adapting to new fraud tactics while minimizing unnecessary disruptions. 

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By implementing RBA, businesses can significantly reduce fraud while ensuring that legitimate customers enjoy a smooth experience.

Leveraging AI/ML to reduce false positives

False positives—legitimate transactions incorrectly flagged as fraudulent—are a major pain point for businesses and customers alike. AI and ML help mitigate this issue through:

• Pattern recognition: AI detects complex fraud patterns that traditional rule-based systems might miss. By analyzing millions of transactions, machine learning models improve over time, reducing false positives.

• Behavioral analytics: ML models analyze user behavior to differentiate between legitimate and suspicious activity. For instance, if a customer consistently shops at a particular retailer or travels to a specific location, AI learns these patterns to reduce unnecessary blocks on transactions.

• Real-time analysis: AI processes transactions in real-time, enabling immediate fraud detection and response. This reduces the risk of false declines, ensuring customers can complete legitimate purchases without hassle.

• Continuous learning: ML adapts to new fraud tactics by learning from historical and real-time data. Fraudsters constantly evolve their methods, and AI-driven fraud systems must evolve accordingly, improving fraud detection accuracy while keeping false positives low.

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Banks can measure AI’s impact on customer satisfaction through key performance indicators (KPIs) such as false positive rates, transaction approval rates, customer feedback, and customer support inquiries related to fraud-related transaction issues.

Tailoring fraud controls for different payment vectors

Modern payment ecosystems must support multiple transaction types, such as (but not limited to) peer-to-peer (P2P) transfers, digital wallets, and card-not-present transactions. Each payment vector requires customized fraud controls while maintaining a unified security framework.

• Risk-based authentication: Adjusts security measures based on transaction risk level. For example, a small recurring subscription payment may require less scrutiny than a high-value wire transfer to a new payee.

• Multi-factor authentication (MFA): Adds an extra layer of security for high-risk transactions. Businesses can implement biometric verification, SMS-based authentication, or authentication apps to verify user identity.

• Transaction monitoring: Uses AI-driven analytics to detect anomalies across different payment methods. Suspicious activities, such as multiple high-value transactions from different geographic locations within minutes, can trigger immediate alerts.

• Collaboration: Engages with financial institutions and regulatory bodies to stay ahead of emerging threats. By sharing fraud intelligence, businesses can collectively improve fraud detection capabilities.

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Metrics for measuring security and customer experience

Because fraud prevention isn’t just about stopping bad actors—but also about ensuring a seamless customer experience–businesses must track metrics beyond standard fraud rates, such as:

• Security effectiveness metrics:

  • False positive rate (legitimate transactions incorrectly flagged)

  • False negative rate (fraudulent transactions that go undetected)

  • Incident response time (time taken to resolve fraud cases)

  • Fraud capture rate (percentage of fraudulent transactions successfully identified)

  • Fraud hit rate (the accuracy of flagged transactions actually being fraudulent)

• Customer experience metrics:

  • User satisfaction scores (collected via customer surveys)

  • Login success rate (indicates ease of account access)

  • Abandonment rate (percentage of users who abandon transactions due to friction)

  • Customer support inquiries (relating to authentication issues and fraud concerns)

By prioritizing these metrics, banks can create and implement a fraud prevention strategy that is both effective and customer-friendly.

The future of payment fraud prevention

As fraudsters continually become more sophisticated, businesses must adopt intelligent, AI-driven fraud controls that strike the right balance between security and customer experience.

By leveraging risk-based authentication, AI-powered fraud detection, and real-time data analysis, banks can stay ahead of emerging threats while ensuring seamless transactions. The future of fraud prevention is dynamic, intelligent, and customer-centric—and banks that embrace these innovations will be best positioned to succeed in the evolving digital landscape.

Contact Galileo to learn how AI/ML can provide secure, seamless financial experiences.