Development of a Real-Time Fraud Detection System for E-Commerce Platforms

The client faces significant challenges with transaction fraud, characterized by highly imbalanced data with a low proportion of fraudulent activities, which hampers detection accuracy. Current manual or rule-based methods result in substantial operational inefficiencies and customer dissatisfaction. The client requires a robust, scalable solution that can identify fraudulent transactions in real time to reduce operational costs and enhance customer experience.

A medium to large online retail company operating in a highly competitive market, aiming to minimize fraudulent transactions and optimize operational efficiency.

• Reduce the cost of fraudulent transaction handling and operational overhead by approximately 24%.
• Improve customer satisfaction metrics related to transaction security and fraud prevention by approximately 36%.
• Analyze a large dataset of transactions (e.g., approximately 140,000 records) with a focus on detecting a small percentage of fraudulent items (around 7%).
• Develop a real-time, monitored machine learning model capable of identifying complex fraud patterns within imbalanced datasets.

• Real-time transaction monitoring and fraud probability scoring.
• Use of advanced classification techniques including Random Forest, Bootstrap, CatBoost, and LightGBM.
• Development of around 80 features capturing transaction patterns and customer behavior.
• Handling highly imbalanced datasets to ensure accurate detection of fraudulent transactions.
• Monitoring system performance and model accuracy over time, with alerts for potential drift.
• Integration with existing eCommerce transaction processing workflows to enable seamless fraud prevention.

• E-commerce transaction systems for real-time data feeding
• Internal analytics dashboards for fraud monitoring and reporting
• Notification systems to alert fraud analysts of high-risk transactions

• High availability and reliability to support real-time processing
• Scalability to handle increasing transaction volumes
• Security measures to protect sensitive transaction and customer data
• Model accuracy with targeted detection of approximately 7% fraudulent transactions within large datasets

The implementation of this real-time fraud detection system is expected to significantly reduce operational costs related to fraud handling by approximately 24%, enhance customer trust and satisfaction by about 36%, and improve overall transaction security. Additionally, the system's ability to analyze large transaction datasets with designed models will enable scalable and efficient fraud prevention tailored to the client’s market needs.