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Finance

Automating Cross-Platform Data Transfers and Validation for a Banking Institution

Focus Areas

Data Migration

Automation

Digital Transformation

Business Problem

The manual nature of data migration and validation processes impacted the accuracy of financial reports and operational efficiency for a large banking institution. It caused significant delays in data availability due to challenges with managing cross-platform data transfers between Oracle and SQL Server databases. The bank was looking for a solution that could ensure data integrity, scalability, and seamless cross-platform transfers for the processes through automation.

Key challenges:

  • Manual Data Transfers: The bank’s existing process required manual intervention for migrating data between Oracle and SQL Servers. This was prone to human error and affected reporting timelines because of delays in the availability of crucial financial data.

  • Data Inaccuracy: The manual nature of the migration process posed significant operational and compliance risks and compromised the accuracy of the financial records due to errors occurring during data validation.

  • Slow Data Processing: Lack of automation resulted in slower processing times and created bottlenecks for the bank’s reporting and transaction processing function for data transfers and validation.

  • Scalability Limitations: The existing infrastructure and manual workflows limited the institution’s ability to process and manage financial data in real-time due to which the banks could not scale efficiently with its growing data volume.

The Approach

Curate’s consultant team solution revolved around automating cross-platform data transfer and validation through SQL Server Integration Services (SSIS) packages and leveraging cloud services to ensure scalability and efficiency in data storage and processing. This required close collaboration with the institution’s internal IT, database, and compliance teams to gain a thorough understanding of the existing challenges. 

Key components of the solution:

  • Discovery and Requirements Gathering: Curate identified the following key requirements through close collaboration with the bank’s internal teams to understand the existing infrastructure, workflows, and difficulties:

    • Reduce manual errors by automating the data migration process between Oracle and SQL Servers.

    • Ensure data integrity and accuracy by implementing validation mechanisms.

    • Scale data storage and processing by utilizing cloud Infrastructure (AWS or Azure).

    • Improve the speed of data availability and processing for financial reporting.

  • Automating data transfers and validation with SSIS: Curate’s solution involved creating SSIS packages that would automate the cross-platform data transfers and validation.

    • Dynamic SSIS packages: Curate developed a series of SSIS packages customized to handle various financial datasets and validated data consistency throughout the process to automate the data migration between Oracle and SQL Server.

    • Automated Data Validation: The SSIS packages also ensured the integrity of financial records through incorporated automated validation scripts. This validation process was necessary to detect and correct discrepancies during migration, thus improving data accuracy.

  • Cloud Infrastructure for Scalability: To accommodate the growing data needs, curate recommended utilizing cloud services for data storage and processing scalability and efficiency.

    • Integrating AWS and Azure: To enable the bank to handle large datasets with increased speed and efficiency, Curate designed a cloud-based infrastructure that utilized AWS or Azure.  This ensured that financial data was readily available for reporting and decision-making, scaling data storage and processing.

    • Data Lake Architecture: By implementing a data lake solution on the cloud, Curate provided the bank with a centralized repository for structured and unstructured financial data. This made financial data accessible faster, supported real-time processing, and improved overall reporting times.

    Process Automation and Optimizing Workflows: To ensure quicker yet error-free operations, Curate introduced process automation, reducing manual intervention in data migration and validation.

    • Automating routine tasks: Curate automated routine data transfer and validation tasks which eliminated manual touchpoints and reduced the risk of human error, ultimately freeing up resources to focus on more strategic initiatives.

    • Real-Time Data Processing: The new architecture allowed the bank to update financial records instantly due to the implementation of real-time data processing, which resulted in quicker transaction processing and timely financial reports.

  • Stakeholder Engagement and Change Management: Curate worked closely with internal stakeholders, external vendors, and platform providers to enable the bank to smoothly transition to the new system.

    • Collaboration with Internal Teams: Curate ensured alignment with the bank’s IT, compliance, and financial reporting teams and addressed concerns throughout the project through regularly scheduled meetings.

    • Vendor Coordination: The Curate team ensured seamless integration of cloud services with the bank’s existing infrastructure through coordination with cloud providers (AWS and Azure).

    • Change Management and Training: Curate provided training sessions to the bank’s internal teams to ensure they were fully equipped to manage the new cloud infrastructure and automated processes.

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Business Outcomes

Enhanced Data Accuracy


The accuracy of financial records was greatly enhanced due to the use of automated validation procedures during data transfers, resulting in a lesser chance of financial reporting errors and compliance violations.

Faster Processing and Data Transmission


The bank was able to cut data transmission delays by 50% by automating data migration procedures. Real-time financial data was now accessible, which improved decision-making and reporting schedules.

Decreased Manual Intervention


Automation reduced operating costs and human error by doing away with manual data validation and transfer. The bank was also able to reallocate resources to more strategic endeavors as a result.

Scalability and Future-Proofing


The bank was able to rapidly expand its data processing capabilities thanks to the cloud-based infrastructure, which made sure the system could manage potential rises in transaction volumes and data complexity without experiencing a decline in performance.

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 4 hours 2 hours 50% reduction
Data accuracy (validation errors) 10 errors/week 1 error/week 90% reduction
Manual intervention in data transfer 100% 10% 90% reduction
Financial data availability Next day Real-time Real-time availability
Scalability (transaction volume) Limited Unlimited (cloud) 100% improvement
**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 Architects: Expertise in AWS and Azure infrastructure design, data storage optimization, and cloud integration.

  • Data Engineers: Experience in cross-platform data migration, SSIS package development, and real-time data processing.

  • DevOps Engineers: Proficient in automating data workflows and managing continuous integration/continuous deployment (CI/CD) pipelines.

  • Database Administrators: Skilled in Oracle and SQL Server database management, performance tuning, and data validation.

  • Project Managers: Expertise in managing cross-functional teams, vendor coordination, and ensuring timely project delivery.

  • Change Management Specialists: Experience in training and supporting teams during infrastructure changes and process automation.

Tools & Technologies

  • Cloud Platforms: AWS, Azure

  • Data Migration & Automation: SQL Server Integration Services (SSIS), Oracle Data Integrator, Talend

  • Real-Time Data Processing: Apache Kafka, AWS Lambda, Azure Data Factory

  • Monitoring & Security: AWS CloudWatch, Azure Monitor, Splunk

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

Conclusion

Curate’s solution successfully reduced data processing times, enhanced accuracy, and eliminated the need for manual intervention by automating SSIS packages combined with cloud-based infrastructure. The bank was able to gain the scalability necessary for future growth in transaction volumes and data complexity by making use of cloud technologies such as AWS and Azure. Curate’s ability to deliver customized, high-performance solutions for financial institutions helped drive operational efficiency, a scalable infrastructure, and enhanced data accuracy for the bank.

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