Payment Service Providers (PSPs) need fraud detection systems that can anticipate and adapt to new types of fraud while managing the complex balance between security and customer experience.
In fact, we've seen this first-hand with clients, where our AI-driven approach is helping to strengthen fraud detection capabilities across multiple jurisdictions.
Of course, every PSP faces different challenges. You might be expanding into new markets, dealing with rising transaction volumes or working to reduce operational costs. That's why we've put together this guide based on our real-world experience implementing AI fraud detection systems.
The evolution of fraud detection in payment services
Traditional fraud detection relied heavily on fixed rules and manual reviews. These systems worked well when payment methods were simpler, volumes were fewer, and fraud patterns were more predictable.
But at a time when fraud and scam complaints hit the highest ever level, they're no longer enough.
Consider a typical rule-based system: if transaction X meets criteria Y, flag it for review. While this approach catches known fraud patterns, it struggles with new threats and generates many false positives. Each false positive means a genuine customer faces delays or rejection—damaging both revenue and reputation.
Rather than following rigid rules, AI and machine learning models analyse thousands of data points in real-time, learning and adapting as new patterns emerge. They can spot subtle connections that human analysts might miss, while reducing false positives by up to 80%.
ML systems also help maintain smooth payment flows even as transaction volumes grow. They adapt to seasonal changes, spot emerging trends, and scale efficiently across different markets and payment types.
This approach is essential for payment providers expanding into new territories or launching new products. Organisations need to strengthen their fraud defences, but they also need a solution that can grow with their business while meeting varied regulatory requirements across multiple jurisdictions.
➡️As an interesting side note, we go into more detail on this topic in our new guide ‘Uncovering hidden vulnerabilities: The key to robust fraud prevention’.
Key challenges in Payment Service Provider security
Speed is critical in modern payments. Customers expect instant transfers, but this leaves minimal time for security checks. A delay of even a few seconds can push customers to choose alternative payment methods. This creates a complex balancing act between thorough fraud checks and maintaining smooth payment flows.
Cross-border transactions add another layer of complexity. Each jurisdiction has its own regulatory requirements, fraud patterns, and customer behaviours. A security approach that works perfectly in one market might miss crucial fraud indicators in another. For instance, a transaction pattern that's normal in the UK – like several small payments to different retailers within an hour – might signal potential fraud in markets where single, larger transactions are more common.
That’s why payment providers need systems that adapt to regional differences while maintaining consistent security standards.
These challenges multiply as payment providers expand into new markets or launch new products. Each expansion increases the attack surface for fraudsters while adding new regulatory requirements and customer expectations to manage.
The power of AI and machine learning in PSP fraud prevention
The good news is, unlike traditional systems, AI and ML models become more effective over time. For instance, legitimate users typically follow consistent behaviour patterns when making payments. ML models can spot tiny deviations from these patterns—changes too small for rule-based systems to detect, yet often indicative of fraud.
In our work with a large payment services provider, we're seeing how ML models can process thousands of data points per transaction in milliseconds. This includes obvious factors like transaction amount and location, but also subtle indicators such as device information, timing patterns, and transaction velocity. The models weigh all these factors simultaneously, producing more accurate risk assessments than traditional methods.
The real advantage comes from the system's ability to adapt. As fraudsters change their tactics, the models automatically adjust their detection patterns. This continuous learning helps prevent fraud losses while reducing false positives that might block legitimate transactions.
Our approach combines this advanced technology with practical business needs. The models we develop don't just detect fraud—they provide clear explanations for their decisions, helping risk teams understand and refine the system's performance. This transparency is crucial for maintaining regulatory compliance and building trust with stakeholders.
Key takeaways for payment service providers
The shift to ML-powered fraud prevention is essential for payment service providers who want to stay competitive while managing risk effectively. Our experience highlights several critical success factors:
Data quality matters. The best ML models can't perform well without clean, comprehensive data. Payment providers need robust data management strategies that capture relevant information while meeting privacy requirements. This foundation supports both current operations and future expansion plans.
Second, internal capabilities determine long-term success. While external expertise can jump-start your fraud prevention efforts, building internal knowledge ensures sustainable results. This includes technical skills in data science and ML, but also expertise in risk assessment and regulatory compliance.
Finally, fraud prevention should support growth, not restrict it. The right approach enables expansion into new markets and the launch of new products while maintaining strong security standards. This is where the flexibility of ML-powered systems proves particularly valuable.
Ready to strengthen your fraud defences with AI and machine learning? Our team of risk specialists and data scientists can help you assess your current capabilities and develop a roadmap for the future.
We understand that every payment provider faces unique challenges. Whether you're expanding into new markets, launching new products, or looking to enhance your existing fraud prevention systems, we'll work with you to create solutions that match your specific needs.
Contact us to:
- Review your current fraud prevention capabilities
- Explore how ML can enhance your security while supporting growth
- Learn more about our approach to knowledge transfer
- Discuss your specific challenges with our experts
➡️Get in touch with our risk team.