Neobank Cuts Fraud by 45% with Real-Time AI Detection Engine
"Developers.dev didn't just give us a model, they delivered a full production system that started saving
us money in the first month. Their team felt like an extension of ours. The 45% reduction in fraudulent
transactions has had a direct impact on our profitability and allowed us to scale with confidence."
Gabriel Lane
CTO, Apex Financial
The Problem: The client's rapid growth made them a prime target for fraudsters. Their
manual review process was overwhelmed, customer experience suffered due to incorrectly blocked
transactions, and chargeback fees were eroding their margins. They needed a solution that was both more
accurate and could scale with their business.
Challenges:
- Integrating a new system into their existing payment processing pipeline with zero downtime.
- Processing millions of transactions in real-time with sub-second latency.
- Reducing the high number of false positives that were frustrating legitimate customers.
- Building a system that could learn and adapt to new, unseen fraud patterns.
The Solution: We deployed a dedicated 'AI / ML Rapid-Prototype Pod' to design and build a
custom, real-time fraud detection engine. Our solution involved four key components: First, we engineered
data pipelines using Kafka to ingest and process transaction data in real-time. Second, we developed a
sophisticated machine learning model using a gradient boosting framework that analyzed hundreds of
behavioral and transactional features. Third, we implemented an MLOps framework on AWS SageMaker for
continuous model monitoring and automated retraining. Finally, we built a management dashboard that gave
their fraud team clear insights into why a transaction was flagged, enabling faster, more accurate
reviews.
Key Outcomes:
- Fraudulent transactions successfully blocked increased by 45%
- Manual review workload was reduced by 70%
- False positive rate dropped by 60%, improving customer satisfaction
