Data-Driven Financial Analysis Solution
Empowering Informed Decisions, Risk Management, and Cost Savings through Advanced Analytics and Visualization
The Client’s Challenge
The client faced challenges in financial data analysis, necessitating the creation of proof of concepts (POCs) and business intelligence (BI) reports for risk analysis, asset allocation strategies, and financial key performance indicators (KPIs). Additionally, they required data visualization using Python and Power BI to enhance insights into asset/portfolio behavior.
Our consultant’s solution tackled these challenges by employing advanced analytics and visualization techniques. We developed POCs and BI reports, performed risk analysis against multiple variables, and devised industry-aligned asset diversification strategies. Descriptive analyses and financial KPI reports enhanced understanding of market risk, while data visualization techniques improved report performance and provided valuable insights.
Implementing this solution boosted data-driven decision-making, yielded actionable insights, and augmented financial report performance for the client.
Features & Results
Financial KPI Reports: Generated descriptive analysis and reports for financial KPIs, enabling a better understanding of market risk versus assets under management (AUM) and providing insights into asset/portfolio behavior.
- Risk Analysis: Enabled comprehensive risk analysis against various variables through providing improved performance of financial reports.
- Designed, developed, and maintained a companywide Python package using the Azure DevOps suite. This package facilitated data acquisition from SQL server, Snowflake, and RESTful APIs, with dynamic setup, versioning, and hosting on Azure DevOps repositories.
- Redesign of Exposure Solution: Refactored the design of the exposure solution into a modular, object-oriented pattern compliant with PEP8 standards. Built RESTful APIs using Fastapi and SQLalchemy for internal and external clients, implementing unit testing and documentation auto-generation using Pytest and Sphinx.
- ETL Development: Built efficient ETL processes using PySpark on Databricks for analytics products. This involved cleaning, standardizing, verifying, and combining diverse data sources before loading them into the target location.
- Data Visualization: Leveraged Python and Power BI to visualize data in a dynamic manner, enhancing report performance. Utilized complex SQL queries for data transformation and integration.
- By addressing the client’s challenges and implementing these key features, this solution enabled the client to make informed financial decisions, optimize asset allocation, and improve risk management strategies.
- Utilized power bi to traced data lineage of critical data elements, identified governance issues and provided recommendations to improve data quality and management processes
- Conducted a root cause analysis of data discrepancies for a dataset acquired from different credit agencies:
- Collaborated with stakeholders to understand the impact of the data quality issue
- Developed and delivered a comprehensive presentation to stakeholders and authored wikis to clearly explain the reasons for the data quality issue and providing recommendations for resolving the problem.
- Collaborated with stakeholders to establish and enforce data governance policies and procedures, including the proper identification and labeling of data sources.
The implementation of this solution delivered significant outcomes and benefits for the client
Outcomes
The solution delivered significant benefits to the client:
- Cost Savings: Achieved a 30% cost reduction through streamlined data acquisition and processing, resulting in annual savings of $300,000.
- Revenue Growth: Experienced a 12% increase in portfolio returns, leading to revenue growth of $2 million annually.
- Operational Efficiency: Reduced data processing time by 40%, enhancing operational efficiency and productivity.
- Enhanced Risk Management: Achieved a 20% reduction in high-risk exposures, improving risk management and safeguarding assets.
- These outcomes highlight the tangible benefits of this data-driven solution, including cost savings, revenue growth, improved operational efficiency, and enhanced risk management capabilities.