ML-Ready Data Pipeline for a High-Growth Fintech Platform
Project Overview
For a fast-scaling fintech startup, Syncortex engineered a real-time data pipeline to power fraud detection and customer scoring models. The system ingested and transformed transactional, behavioral, and third-party data into model-ready formats with automated feature engineering.
Duration
4 months
Team Size
7 specialists
Industry
Fintech
The Business Need: Scaling Risk Intelligence with Real-Time Data
A fast-growing digital lending and payments platform was onboarding thousands of new users each week. While its customer base expanded rapidly, its machine learning infrastructure lagged behind—especially for fraud detection and credit scoring.
Key issues included:
Key Challenges
- Delays in preparing data for model training
- Siloed sources: transactional, behavioral, and third-party enrichment data lived in separate systems
- Manual feature engineering causing long development cycles
- Inability to support real-time scoring for high-risk decisions
- Growing fraud rates as manual review became impossible at scale
The company needed a robust, ML-ready pipeline that could operate in real-time, unify diverse data streams, and dramatically cut model development and deployment times.
The Solution: Real-Time ML Data Pipeline with Automated Feature Engineering
Syncortex designed and implemented a real-time, scalable data pipeline that transformed the fintech's data ecosystem into a continuous intelligence engine—feeding both real-time and batch ML models.
Key elements of the solution:
Streaming Architecture
Built with Apache Kafka and Spark Streaming for ingesting transactional and behavioral events as they occurred—ranging from payment actions to user device activity.
Data Unification and Enrichment
Joined real-time user activity with third-party data (e.g., telecom scores, credit bureau inputs) and unified internal transaction logs and KYC information for complete user profiling.
Automated Feature Engineering
Created dynamic features like velocity metrics, device fingerprinting, and geolocation consistency. Leveraged PySpark for high-speed transformations and feature aggregation.
Feature Store Implementation
Deployed a centralized repository of model features with version control, automated testing, and metadata tracking to ensure consistency between training and production.
Deployment-Ready for Real-Time Scoring
Enabled downstream credit scoring models to operate in sub-second latency—ideal for on-the-fly loan decisions and fraud checks.
The Outcome: ML Agility at Fintech Speed
The pipeline dramatically improved the fintech's ability to detect fraud, assess creditworthiness, and iterate on machine learning models without data bottlenecks.
Key Results
Fraud detection accuracy improved by 27%
Thanks to richer real-time behavioral data and better model input precision.
Model retraining cycle cut from 2 weeks to 2 hours
Enabling rapid experimentation and response to new fraud patterns or market signals.
Instant credit scoring
Enabled for first-time applicants—reducing drop-offs and increasing approval conversion during peak traffic windows.
Data preparation time reduced by 85%
With automated feature engineering replacing manual data science efforts.
The Impact: Real-Time Risk Intelligence, Engineered for Scale
The ML-ready pipeline became a core enabler of the fintech platform's strategic advantage—risk control at speed, scale, and precision.
Long-term Business Benefits
- Customer acquisition surged as approvals became faster and smarter
- Operational risk declined, with early intervention based on predictive alerts
- Data science productivity soared, with reusable features and automated workflows
- 90% reduction in manual review cases despite 3x transaction volume growth
- Engineering resources shifted from data prep to product innovation
- New ML use cases rapidly developed using the same pipeline infrastructure
By building a robust, real-time ML data pipeline, the fintech not only overcame immediate challenges in fraud detection and credit scoring but created a foundation for continuous AI innovation. The infrastructure now serves as a competitive advantage, enabling rapid response to market changes and new risk patterns while supporting the company's ambitious growth targets.
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