The Necessity of Business Intelligence in Finance

In the contemporary financial industry, the necessity for a Business Intelligence (BI) solution is indisputable. Financial companies deal with vast amounts of data on a daily basis, including customer information, transaction records, market data, and more. Business Intelligence (BI) solutions play a crucial role in making sense of this data and turning it into actionable insights.

Key Contributions of BI Solutions

The invaluable contributions of BI solutions span various areas, including but not limited to:

  • Data Management
  • Performance Monitoring
  • Risk Management
  • Compliance and Regulatory Reporting
  • Customer Insights
  • Fraud Detection
  • Cost Management
  • Strategic Decision-Making
  • Market Analysis
  • Forecasting and Planning

Challenges in Implementation and Usage

And while Business Intelligence (BI) solutions offer numerous benefits to the financial industry, they also face serious challenges in their implementation and usage. Some major problems that financial institutions, including traditional banks, neobanks, and fintech companies, may encounter with BI solutions include:

GET Finance Tech BI Team Services

Addressing these challenges requires a comprehensive approach, including investment in technology, data governance, cybersecurity measures, and organizational change management. Therefore GET Finance Tech BI Team can help you with:

  • Assistance in designing complete long-term data strategy
  • Guiding the choice of adequate technologies 
  • Testing, optimization and expansion of existing solutions
  • Data analysis and exploration
  • KPI identification
  • Data solution architecture
  • Data integration
  • Data storage architecture such as warehouse, lake etc.
  • Data modeling
  • Data transformation, such as ETL, ELT etc.
  • Advanced data analytics, including ML
  • Data visualization
  • Operational, management, financial reports etc.
  • Historical and near-real-time data refresh
  • Self-service data analysis
  • Data governance process setup
  • Data quality
  • Data security and privacy
  • Metadata management
  • Data ownership

Technologies