📊 Insurance Analytics Dashboard

Interactive claims, risk, and operational insights for insurance decision‑makers

🧩 Overview

Insurance leaders often struggle with fragmented data across claims, risk indicators, and operational metrics. Without a unified view, it becomes difficult to identify trends, spot anomalies, and make timely decisions. The Insurance Analytics Dashboard consolidates key metrics into a single, interactive interface designed for underwriting, claims, and risk teams. It supports faster decision‑making, better visibility, and more proactive risk management.

🎯 Problem

Insurance executives and analysts lacked a centralized, intuitive dashboard to monitor:

• Claims volume and severity
• Loss ratios and risk indicators
• Operational performance metrics
• Trends across time, region, and product lines This resulted in slow reporting cycles, inconsistent insights, and reactive decision‑making.

🚀 Goal

Build a decision‑support dashboard that provides real‑time visibility into claims and risk performance, enabling leaders to:

• Identify emerging trends
• Prioritize high‑risk segments
• Improve operational efficiency
• Support data‑driven decisions

👤 My Role

• Defined KPIs aligned with business goals
• Designed dashboard layout and visual storytelling
• Cleaned and transformed raw data
• Built interactive visualizations
• Validated insights with business stakeholders

🛠 Approach

1. Data Preparation

• Cleaned and standardized claims and policy data
• Engineered features for severity, frequency, and loss ratios
• Created time‑series and segmentation variables

2. KPI Definition

Aligned with underwriting, claims, and risk teams to define:

• Claims count & severity
• Loss ratio
• Average cost per claim
• Fraud indicators
• Operational cycle times

3. Dashboard Design

Focused on clarity, drill‑downs, and storytelling:

• High‑level KPIs for executives
• Trend charts for analysts
• Segmentation by region, product, and claim type
• Filters for scenario exploration

🧠 Architecture (Conceptual)

Raw Data → Cleaning & Transformation → KPI Engine → Dashboard Layer → User Insights

Tools used: Python, Pandas, Tableau/Power BI (depending on your actual tool), Excel for validation.

🏁 Outcome

• Improved visibility into claims trends and operational performance
• Enabled faster decision‑making for underwriting and claims teams
• Reduced manual reporting effort
• Provided a foundation for predictive analytics and anomaly detection

🔮 What I’d Improve Next

• Add predictive insights (e.g., expected claim severity)
• Add anomaly detection overlays for fraud or outlier claims
• Integrate LLM‑based narrative summaries of dashboard insights
• Add RAG‑based explanations for unusual trends
• Build a self‑serve insights layer for non‑technical users

📁 Repository Structure

/data → Raw and cleaned datasets
/dashboard → Tableau/Power BI files
/scripts → Data cleaning and transformation scripts
README.md → Project documentation

🧑‍💼 Product Context

This dashboard reflects real‑world insurance analytics work, where leaders need:

• A unified view of claims and risk
• Faster insights
• Better operational transparency
• A foundation for AI‑driven decision support