Reducing Credit Card Fraud from 3% to 0.1% with Machine Learning

Client Overview

A major financial services company was facing a critical challenge: an alarmingly high credit card fraud rate of 3%, far above industry standards. This not only resulted in significant financial losses but also put customer trust and business operations at risk.

The Challenge

Traditional fraud detection systems used by the client relied heavily on static rules and basic transaction patterns. These methods failed to detect sophisticated fraud schemes that mimicked legitimate user behavior, especially when fraudsters exploited social connections or gradual behavior changes over time.

The client approached us to design a more adaptive, intelligent solution that could significantly reduce fraud without increasing false positives or degrading the user experience.

Our Solution

We built a custom machine learning model designed specifically for real-time payment fraud detection. Our approach combined behavioral analysis with graph-based features, enabling the system to go beyond individual transactions and detect hidden patterns in user interactions and payment flows.

Key Features of the Model:

• Behavioral Modeling: We analyzed user behavior over time — transaction amounts, frequency, timing, device usage, location patterns — to build a dynamic profile for each user.

• Graph-Based Signals: By modeling the network of transactions between users, we identified unusual or suspicious links that signaled organized fraud rings or compromised accounts.

• Real-Time Scoring: The model scored each transaction in real-time, allowing the client to automatically block high-risk payments or flag them for manual review.

Results

The impact was immediate and transformative:

• Fraud rate dropped from 3% to 0.1% within three months of deployment.

• False positives were reduced, minimizing friction for legitimate users.

• Operational costs decreased, as fewer manual reviews were needed and chargebacks were dramatically reduced.

Conclusion

This project is a testament to how custom-built machine learning models — when grounded in both behavior analytics and graph theory — can radically outperform legacy fraud systems. By partnering with us, the client not only saved millions of dollars but also restored customer trust and operational efficiency.