The Payments Leader’s Guide to AI Fraud Prevention and Detection

How AI Fraud Prevention is Transforming the Way Companies Protect Their Businesses

In the cat-and-mouse game of fraud, the rules are changing. AI is making fraud more sophisticated and easier to conduct. The cost of entry has never been lower. With a few keystrokes, fraudsters can generate a deepfake identity, spin up a realistic-looking phishing site, or manufacture a compelling voice clone. The scale is unprecedented, and the variety of attacks is dizzying.

Over the next 12 to 24 months, emerging AI technologies will dramatically influence how payment professionals approach fraud prevention. This article, part of Rapyd’s “Future Ready: The Guide to AI and Payments” series, will focus on AI’s role in fraud prevention and detection.

Risk and Fraud: The AI-Fraud Battle Intensifies

AI has made fraud easier for criminals. Fraudsters have tools and use generative AI for “do-it-yourself” attacks—creating deepfake emails, videos, and websites. Experian reports that generative AI is lowering the bar for entry into fraud, with attackers now able to orchestrate scams ranging from “proof of life” schemes on social media to identity theft using fake profiles. 

At the same time, AI equips businesses with enhanced defences and new capabilities. For businesses, the solution is clear: fight AI with AI by deploying sophisticated fraud prevention solutions. AI-enabled fraud detection tools will allow companies to stay one step ahead of these sophisticated fraud tactics.

How AI Is Combating Fraud Today – and What’s Next

Fraudsters operate on a simple principle: they exploit predictable patterns of human behaviour. They rely on trust, urgency, and familiarity, knowing that people, even businesses, tend to follow routines. 

This is where AI is already making remarkable strides, processing vast datasets in real-time to identify the telltale signs of fraudulent activity. Machine learning models spot behavioural anomalies, such as an unusual transaction in a foreign country, an account suddenly processing high volumes, or a spending pattern that doesn’t align with past behaviour. And when something doesn’t add up, it immediately flags or blocks the activity.

One significant advancement is in behavioural biometrics. AI models now monitor subtle behaviours—such as typing speed, swipe motions, and how users hold their devices—detecting deviations that may indicate fraud, even if the fraudster has login credentials. Looking ahead, we expect AI models in fraud detection to become even more predictive, self-learning, and responsive, adapting autonomously to new fraud tactics.

Businesses need AI models to learn in real-time, drawing on fresh data to anticipate and counter new fraud tactics. More importantly, continuous monitoring and refinement of these AI systems will allow businesses to predict and combat fraud. AI teams must work alongside payment professionals to ensure models are optimised and ready for new fraud techniques as they arise.

Staying Ahead of AI-Fuelled Fraud

As AI models grow more sophisticated, so do the tactics of fraudsters who utilise the same technology to create complex, hard-to-detect schemes. Deepfakes, for instance, are being used for identity theft, manipulating customer service interactions, and even launching coordinated attacks across multiple platforms. 

Effective fraud prevention strategies combine the strengths of both AI and human expertise:

• Tiered Monitoring: Implement a tiered approach in which AI handles initial threat detection and triage, while human analysts focus on investigating high-priority alerts and complex cases.
• Continuous Improvement: Use human feedback to refine and improve AI models and AI payments over time, creating a cycle of ongoing enhancement.
• Customised Metrics: Develop and monitor custom performance metrics that align with specific fraud prevention goals and risk profiles.

By leveraging AI’s speed and scalability alongside human experts’ critical thinking and adaptability, organisations can create a robust and responsive system capable of addressing current and emerging threats.

Cross-Industry Collaboration for a Stronger Defense

Fraud doesn’t stay confined to one business or sector—many fraudsters use similar methods across industries. By collaborating and sharing anonymised data, companies can train their AI models more effectively, equipping the entire payments ecosystem with stronger defences. The broader the data input, the better prepared AI systems will be to combat increasingly sophisticated fraud techniques, mainly through enhanced AI in payment processing and AI payment gateways.

Fight Fraud and Protect Your Payments

To stay ahead in fraud prevention, business leaders should consider taking the following steps:

1. Form a Specialist AI Fraud Team: Establish a unit dedicated to AI-driven fraud detection and prevention, keeping pace with evolving threats and technologies.
2. Regular Staff Training: Continually update staff on new fraud prevention techniques and AI advancements, enhancing their ability to recognise emerging fraud patterns.
3. Advanced Authentication: Implement robust authentication methods such as biometric verification to strengthen security measures.
4. Strengthen Data Security: Enhance your cybersecurity framework with regular audits, secure data retention practices, and strong encryption to protect against data breaches.
5. Industry Collaboration: Collaborate with peers to share fraud insights and data, improving predictive models and creating a unified defence strategy.
6. Continuous Monitoring: Deploy AI systems to monitor transactions and behaviours in real-time to identify and mitigate suspicious activities.
7. Raise Customer Awareness: Establish ongoing education for customers that helps them understand and prepare against fraud tactics. 

With these measures, businesses can develop a sophisticated, proactive approach to combating AI-driven fraud, effectively neutralising both current and emerging threats.