Manufacturing•7 months

Credit Card Fraud Detection System

Manufacturing company experiencing significant losses from credit card fraud in B2B transactions

Client

Global Manufacturing Company

Duration

7 months

Team Size

9 ML specialists

The Challenge

A global manufacturing company was losing millions annually to sophisticated credit card fraud in their B2B payment processing. Traditional rule-based systems were missing complex fraud patterns, and manual review processes were too slow for real-time transaction processing.

Our Solution

We implemented a sophisticated machine learning-based fraud detection system using Amazon SageMaker for model training and deployment, with real-time inference through AWS Lambda and Kinesis. The system processes transactions in real-time and provides instant fraud scoring.

Implementation Approach

1Historical transaction data analysis and feature engineering
2Machine learning model development using SageMaker
3Real-time inference pipeline with Lambda and Kinesis
4Event-driven architecture for instant decision making
5Model monitoring and automated retraining
6Integration with existing payment processing systems
7Comprehensive fraud reporting and analytics

Technologies Used

Amazon SageMakerAWS LambdaAmazon KinesisDynamoDBCloudWatchAPI Gateway

Results & Impact

✓99.2% fraud detection accuracy
✓Real-time transaction monitoring
✓85% reduction in fraudulent losses
✓Sub-second fraud decision response time
✓Reduced false positives by 60%

“The ML-powered fraud detection system has been a game-changer. We've virtually eliminated fraud losses while improving customer experience.”

— CFO, Global Manufacturing Company

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