Finance

Data Integration and Cloud-Based Trading Engine for a Financial Institution

Focus Areas

Data Integration

IT Infrastructure

Cloud Storage

Business Problem

A prominent financial institution group’s outdated IT infrastructure and legacy systems were posing problems with data integration and slow trade execution. The institution, which managed pooled funds, utilized manual processes to integrate data from multiple systems causing errors, delaying financial reports, and limiting the ability to handle large transaction volumes. The institution was looking for a comprehensive solution to address the dual challenges associated with data integration and trading system scalability.

Key challenges:

  • Inefficient Data Integration: The institution relied on manual processes to integrate data from various legacy systems. This affected the capacity to produce thorough and accurate financial reports, hindered data transfers, and increased the possibility of reporting errors.

  • Slow Trade Execution: The bank’s trading platform’s outdated IT infrastructure was not scalable, which caused trade execution to be delayed and made it more difficult for the bank to effectively handle high asset volumes.

  • Limited Reporting Capabilities: Decision-making was impacted and opportunities were lost due to the lack of real-time data integration and reporting capabilities.

  • Operational Scalability: Because the current system was not scalable, it resulted in operational bottlenecks and dissatisfied customers due to delayed transactions.

The Approach

Curate consultants with the institution’s internal teams to assess the current infrastructure and processes and designed a solution that would address the data integration challenge through automation, and the slow trade execution and scalability through a cloud-based trading engine. Additionally, real-time data analytics would be integrated to enhance financial reporting capabilities.

Key components of the solution:

  • Discovery and Requirements Gathering:  Curate assessed the existing infrastructure and processes and identified the key requirements to solve the bank’s challenges through workshops and consultations in close collaboration with the internal IT, Trading, and Reporting teams: 

    • Reduce manual errors and speed up data transfer between legacy systems by automating the data integration process.

    • Effectively manage large trade and financial transaction volumes by implementing a scalable cloud-based infrastructure.

    • Utilize real-time data analytics and visualization tools to improve reporting.

    • Leverage Azure’s scalable infrastructure and advanced technology stack to improve trade execution speed.

     

  • Data Integration and Azure Data Factory Automation: The solution centered around automating the data integration process using Azure Data Factory.

    • Azure Data Factory: Curate designed pipelines using Azure Data Factory to automate the transfer and transformation of data between legacy systems and SQL server. This eliminated the need for manual intervention and sped up the process, significantly reducing the risk of errors due to data transfer.

    • Azure Data Lake for Centralised Storage: The organization was able to centrally store substantial amounts of both structured and unstructured data by establishing Azure Data Lake as the main storage repository for all financial data.

  • Development of Cloud-Based Trading Engines: Using Azure’s infrastructure, Curate created a cloud-based trading engine to address the problems of scalability and slow trade execution.

    • Scalable Azure Trading Engine: Utilising technologies like ASP.NET for online application development and Azure SQL for fast, safe transaction processing, the new trading engine was constructed on top of Azure’s scalable infrastructure. The engine was built to manage high transaction volumes with low latency.

    • Real-Time Analytics for Trading: Curate incorporated real-time analytics into the trading engine so that the organization could keep an eye on and improve trade execution using real-time market data. This enhanced asset management capabilities by increasing transaction execution speed and accuracy.

    • Security and Compliance: Curate integrated Azure’s security features, such as encryption, firewalls, and multi-factor authentication to ensure that the trading system complied with industry requirements for data privacy and security.

  • Reporting Optimization:

    • Power BI dashboards were implemented to provide decision-makers with real-time insights into financial performance, trading activities, and compliance metrics. 

    • SSRS was used to generate comprehensive financial reports. These reports were automated and combined with real-time data from Azure Data Lake to ensure their accuracy, easy accessibility, and timeliness. 

  • Stakeholder Engagement and Change Management: Curate engaged closely with internal teams, vendors, and stakeholders throughout the project to ensure successful implementation.

    • Internal Team Collaboration: To guarantee alignment with the overall business objectives and technical specifications, Curate held regular meetings with the institution’s trading, finance, and IT departments.

    • Vendor Coordination: To guarantee the smooth integration of cloud services and cutting-edge technology, Curate coordinated with software vendors and cloud service providers (Azure).

    • Training and Change Management: Following implementation, Curate trained the bank’s internal teams on how to handle the new cloud infrastructure, data pipelines, and automated processes. This ensured seamless adoption and continuous operational effectiveness.

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Business Outcomes

Increased Data Accuracy


By automating data transfers using Azure Data Factory and automating validation procedures, financial reporting mistakes were reduced by 85%.

Quicker Trade Execution


Transactions were now processed 70% faster thanks to the new cloud-based trading engine, making it possible for the institution to manage higher trade volumes without sacrificing effectiveness.

Real-time Financial Reporting


Decision-makers were able to act on the most recent information by using financial reports that were generated instantly thanks to real-time data integration and reporting capabilities. Financial reporting now took 60% less time.

Enhanced Scalability


The institution was able to expand its operations and manage higher transaction volumes without operational constraints due to the utilization of Azure's scalable infrastructure. Because of its dynamic scalability, the new system could grow in the future without requiring major reconfigurations.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Data transfer time 6 hours 1 hours 83% reduction
Financial report generation time 48 hours 10 hours 60% reduction
Trade execution time 5 seconds 1.5 seconds 70% reduction
Scalability (transactions per day) 200,000 1,000,000 5x improvement
Data accuracy (errors in reports) 15 errors/month 2 errors/month 85% reduction
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

List of skills, tools, and technologies

The following set of skills, resources, tools, and technologies were used:

  • Cloud Engineers: Skilled in Azure architecture, cloud-based data integration, and infrastructure scalability.

  • Data Engineers: Experience in building data pipelines with Azure Data Factory, data transformation, and real-time analytics integration.

  • Software Developers: Expertise in developing high-performance, scalable trading systems, ASP.NET, and Azure SQL.

  • DevOps Engineers: Proficient in continuous integration/continuous deployment (CI/CD) pipelines and cloud automation.

  • Project Managers: Expertise in cross-functional team management, vendor coordination, and ensuring timely delivery of complex projects.

  • Data Analysts: Skilled in Power BI, SSRS, and generating actionable financial reports.

Tools & Technologies

  • Cloud Platforms: Azure, Azure Data Lake, Azure SQL

  • Data Integration & Automation: Azure Data Factory, Apache Kafka

  • Real-Time Analytics & Reporting: Power BI, SQL Server Reporting Services (SSRS), Apache Spark

  • Security & Compliance: Azure Security Center, encryption protocols, multi-factor authentication (MFA)

  • Collaboration & Project Management: Jira, Confluence, Microsoft Teams

Conclusion

Curate improved operational efficiency and customer satisfaction and resolved the financial institution’s inefficiencies in data transfer, reporting, and trade execution. By automating data integration procedures and creating a scalable cloud-based trading engine using Azure’s cloud infrastructure and cutting-edge tools, Curate enabled quicker trade execution, real-time financial reporting, and enhanced scalability. This allowed the institution to grow and meet the expectations of a rapidly changing financial sector.

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