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Real-Time Fraud Detection System for E-Commerce Platforms

kitrum.com

Financial services

eCommerce

Information technology

Current Challenges in Fraud Detection

E-commerce merchants face significant financial losses due to sophisticated fraud attacks, while traditional detection systems suffer from high false-positive rates and operational inefficiencies. Existing solutions struggle with imbalanced transaction datasets and lack real-time processing capabilities.

About the Client

A fintech company specializing in AI-driven fraud prevention solutions for digital commerce platforms

Strategic Project Goals

Develop a real-time fraud detection system with adaptive machine learning capabilities
Reduce operational costs associated with manual fraud review processes by 24%
Improve fraud detection accuracy while minimizing false positives to increase merchant satisfaction by 36%
Effectively handle highly imbalanced transaction datasets (7% fraud rate)

Core System Requirements

Real-time fraud risk scoring engine
Automated pattern recognition for suspicious transactions
Multi-model machine learning architecture (Random Forest, Catboost, LightGBM)
Feature engineering pipeline with 80+ behavioral indicators
Merchant dashboard for fraud analytics and reporting
API integration for transaction data streaming

Technology Stack

Python

TensorFlow

Scikit-learn

Catboost

LightGBM

Bootstrap

System Integrations

Payment gateway APIs
Merchant transaction databases
Cloud infrastructure (AWS/GCP)
Alert notification systems

Performance Requirements

Sub-200ms transaction processing latency
99.99% system uptime SLA
PCI-DSS compliance
Horizontal scalability for 100K+ transactions/hour
Model retraining pipeline with daily updates

Expected Business Impact

Implementation of this solution is projected to reduce fraud-related losses by 24% while improving merchant satisfaction scores by 36% through faster, more accurate transaction processing. The system will enable merchants to analyze 140,000+ transactions daily with enhanced detection capabilities for the 7% fraud rate baseline.