Optimizing AWS Cloud Infrastructure for Financial Company

CLIENT

NDA

INDUSTRY

Financial Services

SERVICE

Data Modernization / Cloud Development / Data Management

About the Client

Our client is a global leader in the commercial finance services. The organization provides customized lending and risk mitigation solutions to support both international and domestic business growth. 

Overview

Operating through its two primary divisions, Finance and Risk departments, the company relies on multiple internal and third-party data sources to make informed underwriting decisions. However, inconsistencies in client identification across different data providers made it difficult for the client to effectively aggregate, analyze, and leverage critical financial data.

Partnering with Sphere, our partner sought to optimize its AWS cloud infrastructure, improve data retrieval efficiency, and enhance performance across financial decision-making processes.

Challenge

The business faced key operational and technical hurdles that limited the full potential of its data infrastructure:

Data Inconsistencies Across Providers

Multiple third-party data providers referred to the same clients in different formats, codes, and naming conventions, creating mismatches. This inconsistency delayed underwriting decisions and hindered comprehensive risk assessments.

Inefficient Data Aggregation 

Retrieving, processing, and analyzing debtor-related data was slow due to fragmented structures in the existing database. The lack of optimized query structures increased processing time, negatively impacting decision-making speed.

Suboptimal AWS Cloud Utilization

The cloud environment was not fully optimized, leading to unnecessary costs and underperformance in data retrieval speeds. High ingestion time made it difficult to process large amounts of financial data efficiently.

Our Solution

To address these challenges, Sphere executed a structured optimization plan focusing on database evaluation, data warehouse enhancements, and AWS infrastructure adjustments.

1. Database Evaluation & Optimization

Sphere conducted a deep-dive assessment of the client’s current database condition and structure, identifying inefficiencies that hindered performance.

  • Reviewed and optimized table structures to improve query execution and retrieval speeds.
  • Identified redundant data points and recommended indexing strategies for better search performance.

2. Data Warehouse Optimization for Debtor/Client Management

To improve data aggregation, Sphere restructured the debtor and key data elements in data warehouse:

  • Implemented a standardization framework to unify client identification across different data sources.
  • Designed new tables for summarization and quick search, enabling faster debtor-related analytics.
  • Provided a roadmap for long-term enhancements to further refine client’s financial data strategy.

3. AWS Cloud Infrastructure Enhancements

Sphere optimized AWS cloud services by fine-tuning data ingestion pipelines and query execution strategies:

  • Adjusted cloud resource allocations to improve performance while reducing unnecessary costs.
  • Implemented new indexing and caching mechanisms, leading to significant gains in processing speeds.

Result

By partnering with Sphere, our client successfully transformed its AWS cloud infrastructure, improving data retrieval efficiency, financial analytics, and underwriting processes. Through database enhancements, structured data warehouse recommendations, and AWS optimization, the organization now operates with a faster, more cost-efficient, and high-performing cloud environment—delivering better response times and smarter financial decisions while saving on operational costs.

The AWS cloud optimization project delivered measurable performance improvements, enhancing data accessibility and processing capabilities:

  1. 20% reduction in data ingestion time, allowing faster integration of new financial data.
  2. 30% improvement in query performance, enabling real-time insights for underwriting and risk assessments.
  3. More efficient data aggregation and standardization, eliminating inconsistencies across third-party data providers.

Key Achievements

Enhanced AWS Cloud Performance

Streamlined data processes to support faster decision-making in finance and risk assessments.
Data Processing Efficiency Gains

Achieved 20% faster ingestion times and 30% faster query results, reducing operational delays.
Financial Data Standardization

Ensured consistent client identification, improving the accuracy of underwriting and risk assessments.
Cost Savings on Cloud Services

Optimized AWS infrastructure, reducing wasteful spending while boosting cloud efficiency.