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Insurance Analytics End-to-End Data Pipeline

Table of Contents

1. Project Overview
2. Purpose & Scope
3. Business Context (Simulated Insurance Company)
4. High-Level Architecture
5. End-to-End Data Pipeline
6. Analytical & BI Layer
7. Technology Stack & Design Rationale
8. Repository Structure
9. Documentation Index
10. Data Disclaimer

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Project Overview

This project implements an end-to-end insurance analytics data platform, built as a technical showcase for the insurance domain.

The system simulates the full analytical lifecycle of an insurance portfolio — from synthetic data generation to interactive dashboards — with the goal of demonstrating how modern data engineering and analytics capabilities can be applied in insurance environments.

Rather than delivering a production-grade actuarial model, the project focuses on showcasing architecture, data flows, validation logic, analytical schemas, and BI consumption patterns that are commonly required in real insurance analytics initiatives.

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Purpose & Scope

The purpose of this project is to serve as a demonstrative analytical platform for insurance-focused use cases.

Specifically, it aims to:

• Illustrate how insurance-related data could be structured for modern analytics
• Provide a controlled environment to explore insurance KPIs and portfolio metrics
• Demonstrate how raw datasets can be transformed into analytics-ready models
• Enable interactive exploration of metrics through BI dashboards

The underlying data model and assumptions are intentionally simplified. They are not intended to represent a realistic or validated actuarial model, but rather to act as a technical showcase and discussion starting point.

In a real-world engagement, a proper actuarial study, business discovery phase, and domain-specific modeling would be required.

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Business Context (Simulated Insurance Company)

The datasets simulate a fictional insurance carrier:

Company: Eagle Insurance Inc. Market: United States Time Horizon: 2024–2025 Portfolio Size: ~2,000 simulated clients Geographic Coverage: 4 regions, 18 states

The simulated context supports exploratory analysis around:

• Portfolio composition
• Policy and premium distribution
• Claims activity
• Expenses and taxes
• Geographic and product segmentation

The business narrative exists to anchor technical and analytical demonstrations, not to model real insurance behavior.

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High-Level Architecture

The platform follows a layered analytical architecture designed to resemble common insurance data platforms:

Configuration Layer
   ↓
Synthetic Data Generation (RAW)
   ↓
Data Validation & Health Checks
   ↓
Normalization & Transformation
   ↓
Analytical Schema (PostgreSQL)
   ↓
Analytical Views
   ↓
Interactive BI Dashboards

Each layer introduces explicit validation to ensure data consistency and analytical usability downstream.

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End-to-End Data Pipeline

The pipeline is implemented as a sequence of modular Python scripts executed in a controlled order:

1. Dataset Configuration

   • Generates configuration parameters for the simulated portfolio
   • Defines scope, time window, geography, and volume assumptions

2. Synthetic Dataset Generation

   • Produces RAW CSV datasets:

     • Clients
     • Policies
     • Claims
     • Expenses
     • Taxes

3. RAW Dataset Validation

   • Validates file presence
   • Checks row counts and value ranges

4. Normalization & Transformation

   • Cleans and standardizes RAW data
   • Produces analytics-ready dimension and fact tables

5. Database Readiness Validation

   • Verifies database connectivity and permissions
   • Confirms readiness for schema deployment

6. Normalized Data Validation

   • Ensures structural and formatting consistency
   • Validates expected distributions and integrity

7. Schema Deployment

   • Executes a full SQL schema including:

     • Tables and facts
     • Indexes
     • Row-Level Security (RLS)
     • Grants
     • Analytical views

8. Schema Validation

   • Confirms successful schema creation

9. Data Loading

   • Loads normalized data into the warehouse
   • Enforces correct loading order

10. Post-load Validation

    • Confirms data completeness and consistency at database level

Execution details are documented in docs/pipeline.md.

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Analytical & BI Layer

BI Platform

• Google Looker Studio

The BI layer is built on top of dedicated SQL analytical views, each designed to support a specific dashboard or analytical use case.

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Implemented Dashboard

Dashboard 1 — Executive Portfolio Overview

An executive-style overview focused on high-level portfolio indicators, including:

• Portfolio size and evolution
• Premium exposure
• Policy distribution
• Geographic and product segmentation

🔗 Public Interactive Dashboard: https://lookerstudio.google.com/s/ruPQKlKs2CA

📄 Static Snapshots:

• assets/executive_dashboard_annual_nofilters_2024.pdf
• assets/executive_dashboard_annual_nofilters_2025.pdf

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Planned Analytical Dashboards (TBD)

• Claims & Loss Performance
• Operational Metrics
• Risk & Underwriting Insights

Each dashboard is backed by one SQL view per dashboard, maintaining clear analytical boundaries.

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Technology Stack & Design Rationale

Data Warehouse

• Supabase (PostgreSQL) Chosen for:
• PostgreSQL compatibility
• Row-Level Security support
• Structured schema and permission management
• Cost-efficient experimentation

Analytics & BI

• SQL-based analytical views
• Google Looker Studio for interactive reporting

Data Engineering

• Python
• Pandas
• Modular pipeline architecture
• Explicit validation and health checks

The stack and design choices emphasize clarity, reproducibility, and analytical accessibility.

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Repository Structure

.
├── docs
├── output
│   ├── config.json
│   ├── raw
│   └── normalized
├── schema
│   └── schema.sql
├── assets
│   └── executive_dashboard_annual_*.pdf
├── scripts
│   └── pipeline modules & healthchecks
└── utils
    └── database reset utilities

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Documentation Index

The following supporting documentation will be available in /docs in next updates of this repository:

• architecture.md — System architecture
• pipeline.md — Pipeline execution flow
• schema_contract.md — Warehouse schema contract
• data_model_design.md — Analytical data model
• data_dictionary.md — Column-level definitions
• data_quality.md — Validation rules
• dashboards.md — BI and dashboard design
• business_assumptions.md — Modeling assumptions
• supabase.md — Database setup
• env_example.md — Environment variables reference

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Data Disclaimer

All data used in this project is fully synthetic.

• It does not represent real customers, policies, claims, or financial records
• The model is simplified and illustrative
• The project is intended for technical demonstration and exploratory analysis only

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Closing note

This repository represents a technical showcase of an insurance analytics platform, designed to illustrate how data engineering and analytics practices can be applied in the insurance domain, while leaving room for future, domain-specific modeling and actuarial validation if required.

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