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Building Fraud Detection Systems at Scale

By Kayode Olayiwola · June 2026 · 10 min read

In 2024, fraud cost African businesses over $4 billion. At Prembly, we process millions of identity verification requests across 30+ countries. Every single one could be a potential fraud attempt. Here's what I've learned building fraud detection systems that actually work.

The False Dichotomy: Security vs. User Experience

Most people think fraud detection is about building the strictest possible system. Block everything suspicious. Better safe than sorry, right?

Wrong.

The best fraud detection system isn't the one that catches 100% of fraud. It's the one that balances security with user experience. Because here's the reality: if your fraud detection is too aggressive, you'll block legitimate users. And every blocked legitimate user is lost revenue.

Layer Your Defenses

At Prembly, we don't rely on a single fraud detection method. We use multiple layers:

Layer 1: Rule-Based Systems

Start with simple rules. If someone tries to verify 50 identities in one minute? That's probably automated fraud. If an IP address is associated with known fraud rings? Flag it.

Rule-based systems are fast, explainable, and effective for obvious fraud patterns. They're your first line of defense.

Layer 2: Machine Learning Models

ML models catch the subtle patterns that rules miss. Fraudsters evolve their tactics constantly. A good ML model learns from historical fraud data and adapts.

We use ensemble models—combining multiple algorithms to improve accuracy. Random forests for feature importance, gradient boosting for prediction, and neural networks for complex pattern recognition.

Layer 3: Behavioral Analysis

This is where it gets interesting. We analyze how users interact with the system. Fraudsters behave differently from legitimate users. They hesitate before entering stolen information. They switch between tabs to check details. They use different devices.

By analyzing behavioral patterns—typing speed, mouse movements, session duration—we can detect fraud even when all the data looks legitimate.

Layer 4: Network Analysis

Fraud rarely happens in isolation. Fraudsters work in rings, using the same infrastructure, similar patterns, and connected identities.

We build graphs of user relationships—shared devices, IP addresses, payment methods, identity documents. If one node in the network is fraudulent, we investigate connected nodes.

The Challenge: Real-Time Detection

Fraud detection needs to be fast. Users won't wait 30 seconds for a verification check. At Prembly, we process fraud checks in under 200ms. Here's how:

  • Optimize your models: Use lightweight models for real-time inference. Complex deep learning models are powerful but slow. Reserve them for batch processing.
  • Cache intelligently: Store results for known-good users. If someone has been verified before with no issues, skip heavy checks.
  • Fail gracefully: If your ML model times out, fall back to rule-based systems. Never leave users waiting.

The Human Element

No fraud detection system is perfect. You will have false positives. You will miss some fraud. The key is building systems that learn from mistakes.

At Prembly, we have a human review team for edge cases. When the system is uncertain (say, 40-60% fraud probability), humans make the final call. We then feed those decisions back into the model to improve it.

This human-in-the-loop approach achieves the best balance. Machines handle the obvious cases. Humans handle the nuanced ones. Both learn from each other.

Measuring Success

How do you measure the effectiveness of a fraud detection system? It's not just about fraud caught. You need to optimize for:

  • Precision: Of the transactions you flag as fraud, how many are actually fraud? Low precision means too many false positives.
  • Recall: Of all the fraud that happened, how much did you catch? Low recall means fraud is slipping through.
  • Latency: How fast is the system? Users won't tolerate slow fraud checks.
  • User experience: Are legitimate users getting blocked? Track customer support tickets related to false positives.

At Prembly, we maintain 98% precision and 94% recall while keeping latency under 200ms. It took years of iteration to get there.

The Never-Ending Battle

Fraud detection isn't a one-time project. It's an ongoing battle. Fraudsters adapt. Your system needs to adapt faster.

We retrain our models monthly with new fraud patterns. We run adversarial tests to find weaknesses. We analyze every missed fraud case to understand why our system failed.

Building fraud detection systems at scale is hard. But it's also incredibly rewarding. When you prevent a major fraud attempt, when you protect users' money, when you maintain trust in financial systems—that's what makes it worth it.

Building fraud detection systems? I'd love to hear your challenges. Connect with me on LinkedIn.