Real-Time Fraud Detection Pipeline on Databricks for Finco Pay
Finco Pay is a fintech company delivering secure and fast digital payment processing for global financial institutions and enterprises. It focuses on fraud prevention, risk reduction, and safe transaction handling using real-time streaming data. To improve payment security, Finco Pay partnered with Bacancy to build a real-time fraud detection pipeline using Databricks, Delta Lake, and machine learning models.
Real-Time Fraud Detection Pipeline on Databricks
Streaming Transaction Intelligence System
ML-Based Fraud Scoring Engine Framework
Delta Lake Unified Data Architecture Layer
Deal with high transaction volumes and ensure real-time detection of fraud cases
Minimize false positives that affect customers' transactions being processed
Develop scalable streaming pipelines that can adapt to evolving fraud patterns
Maintain consistent feature engineering both in streaming and batch environments
Our Databricks developers designed a unified Lakehouse architecture using Delta Lake to ingest and process real-time transaction streams with reliability and scalability.
We implemented Spark Structured Streaming pipelines to evaluate transactions instantly & generate fraud risk scores based on behavioral signals, patterns, and historical anomalies.
Using MLflow, we managed the full ML lifecycle, including model training, tracking, versioning, and deployment for fraud detection models in production environments.
Our team built a dynamic feature engineering framework that updated fraud indicators such as transaction velocity, IP mismatch, and geo-location deviations to improve detection precision.
Real-time transaction monitoring and fraud scoring engine
Streaming ingestion pipeline using Delta Lake architecture
MLflow-based model lifecycle management
Dynamic feature store for adaptive fraud detection
03
August 2025 - February 2026
35% reduction in false positive rate in fraud detection decisions.
Enabled real-time fraud identification across millions of transactions.
40% Improvement in model accuracy through feature optimization.
5M+ transactions per day handled without performance degradation.
45% increase in fraud detection rate using adaptive behavioral signals.
50% reduction in manual fraud review workload through automation.
| DATA ENGINEERING | Databricks Delta LakeApache Spark Structured Streaming |
| ML & MODELING | MLflow Python PySpark |
| STREAMING | Kafka Integration |
| QUERY LAYER | SQL on Databricks |
| DEPLOYMENT | Databricks Model Serving |
| CLOUD PLATFORM | Databricks Lakehouse Platform |
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