Credit Card Fraud Detection Case Study

Most commercial fraud detection tools rely on autoencoder-based models trained on global transaction patterns. These systems identify anomalies based on generalized definitions of “normal” behavior, which do not accurately reflect regional customer activity. As a result, institutions using such tools face frequent model failures, poor detection rates, and limited adaptability. The lack of contextual relevance and flexibility made it difficult for the client to protect its customers while minimizing operational disruptions.

theMind enhanced the existing modeling techniques and data strategies to build a fraud detection system tailored to the client’s customer base. By incorporating deep generative models, the system extracted more nuanced features and accurately identified risk signals within local transaction data. Unlike generic tools, the solution was developed for in-house deployment—ensuring full control within the client's existing security and hardware perimeter. The result was a more accurate, adaptable, and secure fraud detection system designed for real-world use.