Скачать или смотреть Paisabazaar Fraud Analysis Dashboard in Power BI | AlmaBetter Module 4 – Data Visualization Tools

Welcome to my AlmaBetter Module 4 – Data Visualization Tools Project Explanation, where I walk you through the complete development of a Paisabazaar Banking Fraud Analysis Dashboard in Power BI.

In this detailed video, I explain how to transform a raw financial dataset into a powerful, interactive dashboard that highlights credit risk patterns, customer behavior, delayed payments, EMI burdens, high utilization cases, and potential fraud indicators.

This project demonstrates the practical use of Power BI, DAX, Power Query, and data visualization techniques to analyze financial risk and present insights in a clear, business-ready format.

🔍 What This Video Covers:

✔ Importing and cleaning the dataset in Power BI
✔ Creating calculated columns like:

Age Groups

Income Categories

Credit Utilization Levels

EMI Burden Ratio

Rule-based Fraud Flags
✔ Writing key DAX measures for KPIs:

Total Applications

High-Risk Customers

Fraud Rate (%)

Average EMI

Average Credit Utilization
✔ Designing a fully interactive dashboard with:

Bar Charts

Donut Chart

Scatter Plots

Box Plots

Line Charts

Correlation Heatmap

Top 10 Fraud Flags Chart
✔ Adding slicers for dynamic filtering (Age, Income, Occupation, Credit Score, Payment Behavior, etc.)
✔ Step-by-step explanation of dashboard design and insights

📊 Key Insights You’ll Learn:

✨ Detecting high-risk customers using DAX-based Fraud Flags
✨ Finding patterns in delayed payments & credit behavior
✨ Understanding credit score segments
✨ Identifying risky utilization ratios
✨ Analyzing EMI burden vs income
✨ Storytelling with data through smart dashboard layout
✨ Building a professional one-page BI report

🎓 Skills Demonstrated in This Project:

Power BI Desktop

Data Transformation (Power Query)

DAX Calculated Columns & Measures

Dashboard Design & UI/UX

Fraud Detection Logic

Financial Data Visualization

Insight Storytelling