End-to-End Power BI Project: Loan Default Analytics

An end-to-end walkthrough of building a Loan Default analytics solution in Power BI, sourcing data from Power BI Dataflows (default CSV), performing transformations, modeling with DAX, and creating interactive visuals.

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Table of Contents

1. Project Overview
2. Prerequisites
3. Architecture & Dataflow Setup
4. Data Preparation
5. DAX Calculations
6. Report Development
7. Deployment & Refresh

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Project Overview

This project demonstrates how to:

• Download, install, and configure an on-premises data gateway in Standard Mode
• Import and stage a Loan Default CSV into SQL Server
• Create a Power BI Dataflow from the SQL staging layer
• Connect Power BI Desktop to the Dataflow
• Clean and profile data in Power Query Editor
• Build robust DAX measures for analytics
• Develop interactive Power BI visuals and validate results
• Configure incremental refresh and scheduled data refresh
• Publish the report to the Power BI Service and share with stakeholders

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Prerequisites

• Power BI Desktop (latest version)
• SQL Server instance (local or cloud)
• Power BI Pro or Premium Per User license
• On-premises data gateway installed and registered

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Architecture & Dataflow Setup

1. Gateway Setup: Download, install, and configure the Power BI On-premises Data Gateway (Standard Mode).
2. SQL Server Staging: Bulk-load the Loan Default CSV into a staging table (e.g., dbo.LoanDefaults).
3. Create Dataflow: In Power BI Service, create a Dataflow that connects to the staging table via the gateway.
4. Dataflow Transformations: Apply light transformations (rename columns, enforce data types) and refresh the Dataflow.
5. Connect Desktop: In Power BI Desktop, use Get Data → Power BI Dataflows to import entities from the Dataflow.

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Data Preparation

• Column Definitions & Dataset Description: Document each field (LoanID, ApplicantAge, EmploymentType, AnnualIncome, LoanAmount, LoanPurpose, CreditScoreCategory, DefaultStatus, ApplicationDate).
• Data Types & Profiling: In Power Query Editor, verify data types; use Column Quality and Distribution views to handle nulls, outliers, duplicates.

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DAX Calculations

• Loan Amount by Purpose
  DAX Used: SUMX, FILTER, NOT, ISBLANK
• Average Income by Employment Type
  DAX Used: CALCULATE, AVERAGE, ALLEXCEPT
• Default Rate by Employment Type
  DAX Used: ALL, ALLEXCEPT, COUNTROWS, DIVIDE, FILTER
• Average Loan by Age Groups
  DAX Used: AVERAGE, AVERAGEX, VALUES
• Default Rate by Year
  DAX Used: CALCULATE, COUNTROWS, ALLEXCEPT, FILTER, DIVIDE
• Median Loan Amount by Credit Score Category
  DAX Used: MEDIANX
• YOY Loan Amount Change
  DAX Used: SAMEPERIODLASTYEAR
• YOY Default Rate Change
  DAX Used: subtraction of current and previous period measures
• YTD Loan Amount by Credit Score & Marital Status
  DAX Used: TOTALYTD
• Decomposition Tree Measure
  DAX Used: SWITCH for dynamic breakdowns

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Report Development

• Donut Chart: Average Loan Amount by Age Group & Marital Status (validate proportions).
• Clustered Column Chart: Total Loan by Credit Score Category & Mortgage/Dependents (validate totals).
• Line Charts: YOY Loan Amount and YOY Default Rate Change.
• Scatter Plot: Loan Amount vs. Annual Income with tooltips.
• Decomposition Tree: Drill into key drivers of defaults (CreditScoreCategory, EmploymentType, LoanPurpose).

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Deployment & Refresh

1. Publish: In Power BI Desktop, select Publish → choose target workspace.
2. Dataset Configuration: In Power BI Service, set gateway connection and credentials.
3. Incremental Refresh: Configure partitions on ApplicationDate in the dataset settings.
4. Scheduled Refresh: Set up daily (or desired frequency) refresh under dataset settings.
5. Share Report: Assign report access to stakeholders or embed in Microsoft Teams/SharePoint.