AI-Powered Fraud Detection
In an era of rapid digital transactions across East Africa, a leading fintech enterprise faced escalating fraudulent activities targeting mobile money, card payments, and digital lending platforms. Traditional rule-based systems were catching only 60% of fraud attempts, with a false positive rate exceeding 15% — costing the business millions in KES annually.
Our solution implemented a custom-built neural network architecture capable of processing over 10 million transactions daily in real-time. By leveraging advanced machine learning models trained on 3 years of historical transaction data, we identified complex fraud patterns — including SIM swap attacks, synthetic identity fraud, and coordinated money laundering rings — that traditional systems missed entirely.
Key Deliverables
- Real-time behavioral anomaly detection across mobile money and card channels
- 99.7% detection accuracy with 40% reduction in false positives
- Seamless integration with legacy M-Pesa and card payment gateways
- Automated dispute management and chargeback prevention
The ActivML engine allowed for continuous learning, adapting to new fraud signatures within minutes of discovery. The infrastructure was hardened using Zero Trust principles, ensuring sensitive financial data was handled with the highest level of confidentiality, adhering to ISO 27001 standards throughout the project lifecycle.
Technologies Used
- Machine Learning
- Python
- Cloud Security
- Kafka
Project Details
