Business Analytics project using SQL, Power BI and Power Query for Bank Loan Analysis (Finance Domain)
Bank Loan Analysis | Enterprise-Grade Business Intelligence Project Executive Summary
This repository showcases an enterprise-style Bank Loan Analytics solution designed to mirror how data is consumed, validated, and presented in FAANG-scale and consulting-led analytics environments.
The project demonstrates end-to-end ownership of a financial analytics workflow—spanning data ingestion, transformation, SQL-based validation, time intelligence, KPI design, and executive-ready visualization using Power BI.
The outcome is a decision-focused, production-aligned BI asset that enables stakeholders to assess portfolio health, credit risk exposure, and repayment performance with confidence.
Business Problem Statement
Financial institutions must continuously balance loan growth, capital efficiency, and credit risk. Raw loan data alone does not provide the visibility required to:
Monitor portfolio quality
Track repayment effectiveness
Identify high-risk segments early
Compare period-over-period performance
This project addresses that gap by converting granular loan data into actionable intelligence through standardized KPIs, validated metrics, and interactive dashboards.
Analytical Objectives
Measure loan demand and funding efficiency
Evaluate repayment performance and recovery
Segment Good vs Bad Loans for risk visibility
Perform MTD / PMTD trend comparison
Analyze borrower risk using Interest Rate and DTI
Enable multi-dimensional analysis by region, purpose, tenure, and borrower profile
Data & Methodology Data Source
Loan-level financial dataset (CSV)
Includes borrower attributes, loan terms, repayment metrics, and status flags
ETL & Data Engineering (Power Query)
Schema standardization and data type enforcement
Handling missing and inconsistent categorical values
Feature readiness for time intelligence
Metric normalization (interest rate, DTI)
Performance-optimized data model
SQL as a Validation Layer
SQL was used as a single source of metric truth to:
Validate Power BI KPIs
Cross-check aggregations and percentages
Ensure financial and analytical accuracy
Metrics validated include:
Total Loan Applications
Funded vs Received Amounts
Average Interest Rate and DTI
Good / Bad Loan percentages
MTD and PMTD calculations
Power BI Dashboard Design
The dashboard is structured for executive consumption:
KPI scorecards for instant portfolio health assessment
Time-series trend analysis
Risk segmentation via loan status
Regional and demographic drill-downs
Fully interactive slicers and cross-filtering
The design prioritizes clarity, consistency, and decision velocity.
Business Impact
This solution enables:
Faster identification of high-risk loan segments
Transparent monitoring of repayment efficiency
Data-backed portfolio optimization discussions
Improved stakeholder trust through validated metrics
The framework is scalable and adaptable for real-world banking environments.
Repository Structure ├── data │ └── financial_loan.csv ├── sql │ └── bank_loan_analysis.sql ├── powerbi │ └── bank_loan_analysis.pbix ├── docs │ ├── Bank Loan Analysis Project.docx │ └── Domain Knowledge Doc.docx └── README.md Core Competencies Demonstrated
Business Intelligence & Analytics Thinking
SQL for Metric Validation
Power BI Dashboard Engineering
ETL & Data Modeling
Time Intelligence (MTD / PMTD)
Financial & Credit Risk Analytics
Executive-Level Data Storytelling
Recruiter-Friendly README (Short Version) Bank Loan Analysis | SQL + Power BI
A production-style BI project analyzing bank loan performance, credit risk, and repayment efficiency.
Highlights:
End-to-end analytics ownership
SQL-validated KPIs
MTD / PMTD time intelligence
Good vs Bad loan risk framework
Executive-ready Power BI dashboard
Tech Stack: SQL | Power BI | Power Query | Financial Analytics