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Development of Cloud-Agnostic ML-Powered Transaction Processing System
  1. case
  2. Development of Cloud-Agnostic ML-Powered Transaction Processing System

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Development of Cloud-Agnostic ML-Powered Transaction Processing System

n-ix.com
Financial services
Fintech

Business Challenges in Transaction Processing

The client faced challenges in centralizing transaction decision-making, handling peak transaction volumes efficiently, ensuring compliance with sanctions/AML/fraud regulations, and integrating 15+ fragmented ML models. Existing systems suffered from 5-minute data processing latency, manual deployment pipelines, and inability to scale effectively during high loads.

About the Client

Provider of prepaid cards and current accounts with 1M+ customers and billions in processed payments

Project Goals for Automated Transaction Handling

  • Centralize and automate transaction decision-making using ML
  • Achieve real-time processing with <250ms latency
  • Ensure compliance with financial regulations (AML, sanctions, fraud)
  • Consolidate 15+ fragmented ML models into a unified solution
  • Implement cloud-agnostic infrastructure with scalable architecture

Core System Functionalities

  • API-driven transaction risk assessment (fraud/AML detection)
  • Real-time ETL pipelines for data preparation
  • ML model versioning and serving via BentoML
  • Feature store database for storing calculation history
  • Automated CI/CD pipeline for ML model deployment
  • Integration with Kafka for streaming data processing

Technology Stack

Apache Flink
Kafka
Debezium CDC
MLFlow
BentoML
Docker
Kubernetes
Octopus Deploy

System Integrations

  • Internal banking systems (operational/analytical databases)
  • External credit scoring APIs
  • ETL data pipelines
  • Customer identity verification systems
  • Compliance monitoring frameworks

Non-Functional Requirements

  • Process 1M+ transactions/month with <250ms latency
  • High availability (99.99% uptime)
  • Data anonymization for compliance
  • Horizontal scalability for peak loads
  • Real-time monitoring dashboard

Expected Business Impact

Implementation of this solution is expected to reduce transaction processing latency by 99.17% (from 5 minutes to 250ms), eliminate manual transaction reviews, decrease operational costs by 40% through automated ETL pipelines, increase customer satisfaction (35-point NPS improvement potential), and enable 20% annual customer growth through improved service efficiency. The cloud-agnostic architecture will also reduce vendor lock-in risks while maintaining regulatory compliance.

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