Fraud Detection & Risk Analytics

Led the optimization of an enterprise transaction monitoring system designed to balance rigorous fraud detection with a frictionless customer experience. By transitioning from static rule sets to dynamic, behavior-based risk scoring, we drastically reduced the volume of false positives—saving significant operational costs—without compromising our ability to intercept true fraudulent activity.

The existing fraud detection engine relied on overly aggressive, static risk rules. As transaction volume grew, this approach generated a massive surge in "false positives"—legitimate customer transactions being flagged and blocked. This created a dual crisis: a massive backlog for the manual investigation team (driving up operational costs) and severe friction for customers (leading to support complaints and potential account closures).

We conducted a deep-dive analysis of historical transaction logs paired with investigator review outcomes to understand why legitimate transactions were being flagged. Key insights included:

• The Velocity Trap: Strict thresholds on transaction frequency ("velocity") penalized active, high-net-worth users. A user making three purchases in an hour was treated the same as a bot running a card-testing script.
• Context Blindness: The system failed to incorporate contextual signals. For example, it ignored whether a transaction originated from a "trusted device" or if the geolocation matched the user's historical norms.
• Rule Decay: Many legacy rules had degraded in performance over time, capturing noise rather than evolving fraud vectors.

To modernize the risk engine, we implemented the following strategies:

• Dynamic Behavioral Profiling: Shifted focus from absolute thresholds to relative anomalies by establishing a rolling behavioral baseline for each user (e.g., typical spend amounts, frequent merchant categories, geographic footprint).
• Device & Identity Trust: Integrated a device intelligence layer to apply "step-down" authentication for recognized, trusted devices, allowing low-risk transactions to clear instantly.
• Machine Learning Scoring: Deployed an ensemble machine learning model to generate a real-time risk score (0-100), combining hundreds of variables, replacing binary rule triggers.

The transition to a dynamic risk model delivered immediate and sustainable benefits:

• 40% Decrease in the false positive rate, dramatically improving the user experience for legitimate customers.
• 60% Reduction in the manual investigation backlog, allowing the compliance team to focus on complex, high-risk cases.
• Maintained 99.8% true fraud detection accuracy, proving that security and user experience are not mutually exclusive.