The Challenge
Our client, a digital transactions handler, suffered from a rigid rules-based transaction processing engine. At 8,000 transactions per second, it flagged too many legitimate buyers, driving customer support times up. The compliance desk spent more than 40 hours a week hand-clearing reports, slowing payments and harming the user experience.
The Approach
Predectivera designed a hybrid stream-processing and classification model. We migrated historical ledger transactions into a clean training set, engineered features reflecting buyer behavior (location delta, purchase sequence anomalies), and constructed an auto-scaling ML MVP using Scikit-Learn pipelines. This model scores events and forwards only high-risk flags to human reviewers.
Implementation
Our team implemented a high-performance ingest network using Apache Kafka and Apache Spark. The Spark streaming app queries a Redis cache for customer profiles and feeds data into our Python XGBoost classification layer. The entire containerized cluster auto-scales on AWS, securing steady 80ms latency checks during heavy loads.
The Result
During the first month of deployment, false flagging fell by 94%, letting the client process 99.2% of payments instantly. Compliance support queue sizes collapsed by 85%, freeing operations staff for high-value client relations. The pipeline remains stable under peak traffic loads of 15,000 tx/sec.
"The real-time fraud pipeline developed by Predectivera has saved us hundreds of thousands of dollars in fraud preventions. Their engineering team is top-tier."
Amit Sharma Head of Risk & Fraud Operations, FinPay Corp