Development of a Scalable Automated Data Infrastructure for Enhanced Risk Management in Financial Services

Financial services

Complex Data Pipelines Hindering Scalability and Efficiency in Financial Data Management

The client faces fragmented and unreliable data pipelines that hinder consistency and scalability across teams. Manual data processing tasks consume over 20 hours monthly, leading to delays in operations and reporting. Legacy systems and modern cloud components lack seamless integration, impeding rapid decision-making and operational agility.

About the Client

A large financial institution or fintech enterprise managing extensive data workflows requiring reliable, automated, and scalable data processing and reporting systems.

Goals for Improving Data Automation, Reliability, and Business Insight Timeliness

Implement a fully automated data ingestion, transformation, and delivery platform to reduce manual effort by at least 20 hours per month.
Enhance data processing speed by over 35% to facilitate faster reporting and decision-making.
Improve data quality and reliability to support accurate and timely strategic reporting.
Design a scalable, modular architecture that simplifies future feature expansion and integration with existing legacy systems.
Provide increased visibility into data lineage, performance, and quality metrics to enable proactive data management and troubleshooting.

Core Functional Specifications for Automated Financial Data Infrastructure

Automated data flow management covering ingestion, transformation, and delivery processes.
Data cleaning, structuring, and transformation capabilities to produce available real-time, structured datasets.
Modular architecture employing containerization and orchestration for scalability and ease of future updates.
Error handling with rollback mechanisms to maintain data integrity and system resilience.
Dashboards for monitoring data pipeline performance, data lineage, and quality metrics.

Technology Stack and Architectural Approach for Robust Data Management

Apache Airflow for orchestration of data workflows

Cloud-native services (e.g., GCP-specific tools) for infrastructure and data analytics

Infrastructure as Code tools (Terraform) for environment provisioning

Containerization and registry management for deployable components

Data transformation tools such as DBT for data modeling

Essential External System Integrations for Data Ecosystem Connectivity

Legacy systems for data ingestion and integration
Cloud data warehouses (e.g., BigQuery or similar) for centralized analytics
Monitoring and observability platforms for performance tracking

Non-Functional Requirements Ensuring System Reliability and Scalability

System scalability to handle increasing data volume without degradation in performance
Achievement of over 35% faster data processing times
High availability with autoscaling capabilities
Data security and compliance standards appropriate for financial data
Robust error handling and rollback procedures

Projected Business Benefits from the Data Infrastructure Enhancement

The new automated data platform is expected to significantly reduce manual effort by over 20 hours monthly, improve data processing efficiency by more than 35%, and enhance data reliability. These improvements will enable faster, more informed decision-making, support more agile experimentation for analytics teams, and establish a solid foundation for scalable and dependable risk management and strategic reporting in the financial sector.