This project focuses on analyzing customer credit risk using SQL-based rule logic and visualizing insights with Power BI.
The goal is to identify risk patterns, detect inconsistencies in credit decisions, and support data-driven risk management.
Banks need to evaluate customer credit risk in order to minimize financial losses and make informed lending decisions.
In this project, a dataset of bank customers was analyzed to:
• Segment customers into different risk categories
• Detect inconsistencies between risk level and loan interest rates
• Identify potentially risky customers
• Provide insights for better credit risk monitoring
Customers were categorized into three main groups:
- Low Risk
- Medium Risk
- High Risk
The classification was implemented using SQL rule-based logic (CASE statements).
- MySQL – Data analysis and risk segmentation
- SQL (Views, CASE statements) – Rule-based logic
- Power BI – Interactive dashboard and visualization
The analysis focuses on several important questions:
• How are customers distributed across risk categories?
• Are interest rates aligned with the actual customer risk?
• Are there inconsistencies in credit decisions?
• Which customers represent higher financial risk?
The results of the analysis were visualized in a Power BI dashboard to support monitoring and decision-making.
Key dashboard components include:
• Risk distribution overview
• Credit amount analysis
• Interest rate comparison by risk level
• Customer segmentation insights