Banking Analysis

In banking data analysis, Azure is used to securely store and process large volumes of financial data through services like Azure SQL Database. SQL is used to extract, clean, and manage structured data related to loans, transaction histories, and customer profiles.

Python is then used for deeper data analysis and predictive modeling, such as:

Forecasting loan default risk.

Analyzing customer transaction behaviors.

Segmenting customers based on financial patterns using clustering techniques.

Finally, Power BI is used to create interactive dashboards and visual reports that provide clear insights for decision-makers. These dashboards help bank managers:

Track loan product performance.

Identify high-value customer segments.

Monitor unusual or suspicious transaction activity.