📊 PRISM Insurance Analytics Dashboard

🚀 Project Overview

This project features an interactive analytics dashboard for PRISM Insurance Pvt. Ltd., a fictional insurance company. It transforms raw insurance data into actionable insights, helping stakeholders monitor key metrics like premiums, claims, customer feedback, and policy performance. Designed for data-driven decisions, it uncovers trends in claims processing and highlights customer service improvement areas.

The dashboard processes datasets including policy details, customer demographics, claim histories, and sentiment-analyzed feedback. With advanced visualizations, it offers a user-friendly interface to explore complex data, cutting manual reporting time and revealing efficiency opportunities.

🎯 Objectives

• Visualize core metrics: Aggregate and display premiums 💰, coverage 📦, and claims 📋 for a quick business health overview.
• Analyze customer feedback: Use sentiment analysis 💡 and word clouds ☁ to spot common pain points and positive experiences.
• Track claims and policies: Monitor claim statuses ✅, policy types 📜, and demographic trends 👥 to identify inefficiencies.
• Support interactive exploration: Enable filtering 🎛, drill-downs ⬇, and custom views for deeper analysis.
• Enhance portfolio showcase: Showcase expertise in data visualization 🎨, ETL processes 🔄, and BI tools 💻.

🔧 Technologies Used

Environment: Power BI Desktop, deployable to Power BI Service for sharing.

📊 Key Features and Visualizations

The dashboard spans multiple pages for focused analysis, including an overview, detailed data, and feedback sections.

1. Executive Summary Metrics 📊

• Total Premium Amount: 5.98M 💰
• Total Coverage Amount: 600.55M 📦
• Sum of Claim Amounts: 16.91M 📋
• Displayed as bold cards with conditional formatting to flag variances ⚠.

2. Policy and Demographic Breakdowns 📜👥

• Premium Amount by Policy Type: Bar chart showing Travel (2.5M), Health (1.2M), Auto (1M), Life (0.7M), Home (0.6M) 🚗🏠.
• Active vs. Inactive Policies: Pie chart with 25.09% Active and 74.91% Inactive, aiding retention analysis.
• Gender Distribution: Cards showing 5001 Females 👩 and 5003 Males 👨, expandable by age groups (Young Adult, Adult, Elder).

3. Claims Analysis 📋

• Number of Claims by Claim Status: Column chart with Rejected (8.4K), Settled (3.4K), Pending (2.3K) ✅❌⏳.
• Claim Amount by Age Group: Area chart trending claims across Young Adult, Adult, Elder, showing higher claims in older groups 👴.
• Claims Summary Table: Crosstab by Policy Type, e.g., Auto: Pending 20,810.53, Rejected 40,671.59, Settled 32,984.58.

4. Customer Feedback and Sentiment Analysis 😊💬

• Word Cloud Visualization: Highlights terms like "Policy," "Claims," "Customer Service," "Helpful," "Frustrating," "Quick" from feedback text ☁.
• Feedback Categorization Bar Chart: Counts Excellent (54) 😊, Needs Improvement (39) ⚠, Good (4) ✅.
• Detailed Feedback Table: Lists names, sentiment scores (0.01-0.07), e.g., Aaron Collins: "Website down, inconvenient," Amanda Scott: "Policy rates increased, not happy."

5. Interactive Data Table 📂

Paginated view of raw data (PolicyNumber, CustomerID, ClaimNumber, Age, Gender, etc.) with filters 🎛 and sorting 🔧.

6. Advanced Interactivity 🔍

• Filters and Slicers: Dynamic updates across visuals by Age Group, Claim Status, Gender, Policy Type 🎛.
• Drill-Through Capabilities: Click visuals to explore detailed pages (e.g., claims, feedback) ⬇.
• AI-Enhanced Insights: Sentiment scoring 😊 and key influencers for claims rejection trends 💡.

⚙️ Challenges and Solutions

• Challenge: Inconsistent data formats in feedback 💬 and dates 📅.
  Solution: Power Query cleaned data 🧹, with text analytics for sentiment 💡.
• Challenge: Avoiding visual overload 🎨.
  Solution: Multi-page layout with tooltips ℹ and cross-highlighting 🔗.
• Challenge: Performance with large datasets 📊.
  Solution: Optimized DAX 📉 and aggregated models for speed ⚡.

📈 Results and Impact

• Efficiency Gains: Cut report generation from hours to minutes with automation 🤖.
• Insights Uncovered: Spotted high Health policy rejections and themes like "unclear terms" for improvement ⚠.
• Scalability: Adapts to new data seamlessly 🌐.
• Personal Growth: Boosted skills in BI, data storytelling 📖, and user design 🎨.

📑 Pages

• Page 1: Customer Feedback Page – Word Cloud ☁, Feedback Table 💬, Categorization Chart 📊.
• Page 2: Detailed Data Table – Policy and Claim Records 📂 with Filters 🎛.
• Page 3: Overview Dashboard – Summary Metrics 📊, Policy Breakdowns 📜, Claims Visuals 📋.