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The Credit Risk Analytics Project leverages SQL for data manipulation and Power BI for visualization, providing stakeholders with actionable insights into loan applications and approvals. It features optimized queries using CTEs for efficient processing and dynamic query handling for automatic data updates.

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Credit Risk Analytics Project

Overview

The Bank Loan Report Project leverages SQL for data preprocessing and manipulation and Power BI for visualization, providing stakeholders with actionable insights into loan applications and approvals. It features optimized queries using CTEs for efficient processing and dynamic query handling for automatic data updates. The Project aims to analyze and visualize loan data efficiently and dynamically. This involves firing optimized SQL queries to extract and transform data, followed by developing interactive and insightful Power BI dashboards to monitor Key Performance Indicators (KPIs) related to loan disbursements, statuses, and trends and at the end delivered data driven insights in Credit/Loan Disbursement aligned to Project goals.

Tools

SQL:

For data extraction, transformation, and loading (ETL).

Power BI:

For data visualization and dashboard creation.

PostgreSQL:

As the database management system.

Dashboards

The Power BI report consists of three main pages:

Summary:

Provides an overview of key metrics and trends.

Overview:

Offers detailed insights into loan statuses, funding amounts, and amounts received.

Detail:

Allows for in-depth analysis with interactive filters and slicers.

Power BI Implementation

Final Data Preparation

KPIs Measures

Total Loan Applications:

Good and Bad Loan Analysis

Good Loan Percentage:

Bad Loan Percentage:

Data Validation

Data validation was performed using SQL queries to ensure accuracy and consistency of the data used in the Power BI reports. The queries were designed to dynamically fetch the latest data and handle data updates efficiently.

Conclusion

This project demonstrates the power of combining SQL and Power BI to create dynamic, optimized, and insightful loan disbursement reports. By leveraging SQL for robust data extraction and transformation, and Power BI for interactive visualizations, the project provides a comprehensive solution for monitoring and analyzing loan data effectively.

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The Credit Risk Analytics Project leverages SQL for data manipulation and Power BI for visualization, providing stakeholders with actionable insights into loan applications and approvals. It features optimized queries using CTEs for efficient processing and dynamic query handling for automatic data updates.

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