[ [ Financial Technology · 6 months · 3 engineers ] ]
Financial Technology
A fast-growing fintech processing over 2 million transactions daily needed a smarter way to detect and prevent fraudulent activity without increasing false positives that frustrated legitimate customers.
The existing rule-based fraud system flagged too many legitimate transactions, causing customer churn and operational overhead. Manual review queues were growing faster than the team could handle, and new fraud patterns were slipping through static rules.
DHDTech.io embedded a team of two ML engineers and one data engineer who designed and deployed a real-time fraud detection pipeline. Using gradient-boosted models and graph-based anomaly detection, the system learns continuously from new transaction patterns. The solution was integrated with the client's existing microservices architecture and deployed on AWS with sub-100ms inference latency.
DHDTech.io's team didn't just build a model -- they transformed how we think about fraud prevention. The system pays for itself every month.
— VP of Engineering, Confidential Fintech Client
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