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Finlytics Inc. Fintech

AI-Powered Analytics Platform for Fintech Leader

A growing fintech company requiring real-time analytics and fraud detection across millions of daily financial transactions.

80% reduction
in query latency (from 250ms to under 50ms)
#2
Real-time fraud detection covering 99.97% of transactions
40% reduction
in infrastructure costs through optimized data pipelines
#4
Successfully processed 5M+ transactions daily with 99.99% uptime
The Challenge

The client needed a real time analytics platform capable of processing millions of transactions per second while maintaining sub-50ms latency for fraud detection queries.

ObjectivesWhat we set out to achieve
Process 5M+ daily transactions with sub-50ms query latency
Enable real-time fraud detection across all transactions
Reduce infrastructure costs while improving performance
Achieve 99.99% platform uptime for mission-critical operations
Our ApproachDiscovery, Design, Architecture & Development
Step 1Discovery

We conducted a 3-week discovery phase mapping data flow requirements, analyzing existing infrastructure bottlenecks, and defining performance benchmarks for the new platform.

Step 2Design

Designed a distributed stream-processing architecture with Apache Kafka at the core, enabling horizontal scaling and fault-tolerant data processing across multiple availability zones.

Step 3Architecture

A multi-layered architecture with Kafka for stream ingestion, custom ML models for fraud scoring, PostgreSQL for transactional data, Redis for caching, and Kubernetes for orchestration.

Step 4Development

Over 12 weeks, we shipped the MVP with core analytics, followed by 6 weeks of optimization. The team worked in 2-week sprints with continuous integration and automated deployment.

Technologies Used
PythonApache KafkaTensorFlowKubernetesPostgreSQLRedis
Key Features
Real-time transaction analytics dashboard
Machine learning fraud detection engine
Automated alerting and reporting system
Horizontal scaling infrastructure
Technical HighlightsEngineering excellence in action
Custom TensorFlow models for fraud scoring with sub-50ms inference
Kafka stream processing handling 50K+ events per second
Kubernetes auto-scaling for variable transaction loads
Challenges SolvedComplex problems we overcame
Migrated from legacy batch processing to real-time streaming without data loss
Optimized ML model inference to meet sub-50ms latency requirements
Designed fault-tolerant architecture that maintained operations during node failures
Performance ImprovementsMeasurable metrics
Query latency reduced from 250ms to under 50ms (80% improvement)
Infrastructure costs reduced by 40% through optimized resource utilization
System throughput increased from batch to real-time 24/7 processing
Business Impact

The platform enabled the client to offer real-time fraud detection as a competitive differentiator, reducing fraud losses while improving customer trust through instant transaction verification.

Project Gallery

“DiVentra Labs rebuilt our entire analytics infrastructure in just 10 weeks. The platform now processes 5M transactions daily with sub-50ms latency - something our previous vendor couldn't achieve in 18 months.”

D

Daniel Foster

CTO, Finlytics Inc.

Project Details

Client

Finlytics Inc.

Industry

Fintech

Results
80% reduction in query latency (from 250ms to under 50ms)
Real-time fraud detection covering 99.97% of transactions
40% reduction in infrastructure costs through optimized data pipelines

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Full ResultsDetailed outcomes achieved
80% reduction in query latency (from 250ms to under 50ms)
Real-time fraud detection covering 99.97% of transactions
40% reduction in infrastructure costs through optimized data pipelines
Successfully processed 5M+ transactions daily with 99.99% uptime
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