Case Study - Real-time fraud detection for financial institutions
Phobia is a fintech security platform that uses AI-powered behavioral analysis to detect and prevent fraudulent transactions in real-time.
- Client
- Phobia
- Year
- Service
- Platform development
Overview
Phobia needed to modernize their fraud detection platform to keep pace with increasingly sophisticated attack vectors. Their legacy system was generating too many false positives, creating friction for legitimate users while missing emerging fraud patterns.
We worked with Phobia to architect and build a new detection engine from the ground up. The platform analyzes behavioral patterns, transaction velocity, device fingerprinting, and network analysis to identify fraud in real-time with greater accuracy.
Our AI-assisted development process allowed us to rapidly iterate on machine learning models, testing different approaches and optimizing performance. We implemented a microservices architecture that could scale to handle millions of transactions per day while maintaining sub-100ms response times.
The platform now serves as the backbone of Phobia's fraud detection offering, protecting financial institutions and their customers from sophisticated attacks.
What we did
- Machine Learning
- Microservices
- Real-time Processing
- Cloud Infrastructure
- API Development
Connectico's expertise in both machine learning and scalable architecture was invaluable. They helped us build a platform that's both more accurate and more performant than our previous system.
CPO of Phobia
- Reduction in false positives
- 60%
- Detection accuracy
- 99.7%
- Average response time
- <100ms
- Daily transactions processed
- 5M+