🧾 Insurance Claims Risk Dashboard Project

📌 Project Overview

This project demonstrates how to analyze insurance claims data by applying a complete actuarial data science workflow using Python and Tableau. It showcases data cleaning, feature engineering, fraud detection, and stakeholder-ready visualizations.

The goal: Identify potential fraudulent activities, assess customer risk profiles, and deliver insights to stakeholders through a clear visual dashboard.

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🔁 Workflow: Ask → Prepare → Process → Analyze → Share → Act

✅ ASK

The goal of this project was to explore the feasibility of using basic feature engineering and rule-based risk scoring to flag potentially fraudulent insurance claims in a simplified dataset.

The focus was on:

• Cleaning and merging claims and customer data
• Creating basic risk flags using thresholds
• Testing with a synthetic fraudulent claim
• Building a proof-of-concept Tableau dashboard for stakeholder presentation

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🧹 PREPARE

• Loaded raw datasets: Insurance_Claims_Data.csv and Insurance_Customers_Data.csv
• Inspected for missing values, data types, and duplicates
• Standardized column names and merged datasets on customer_id
• Saved the result to Cleaned_Merged_Insurance_Data.csv

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🧪 PROCESS

• Feature engineered new metrics:
  • claim_to_premium_ratio
  • is_high_risk (binary flag)
  • processing_efficiency
• Exported as Feature_Engineered_Insurance_Data_with_ClaimID.csv

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🔍 ANALYZE

• Generated synthetic claim_id values in the format CL101, CL102, etc.
• Injected a controlled fake fraudulent record to test risk scoring logic
• Created fraud detection logic based on:
  • High claim amount thresholds
  • Claim frequency
• Flagged high-risk and potentially fraudulent claims
• Final dataset saved as: Final_Insurance_Claims_Data.csv

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📊 SHARE

• Created a stakeholder-facing dashboard using Tableau Desktop & Tableau Public
• Included KPIs and visual insights:
  • Total Claims
  • Total Fraud Claims
  • Percent Fraud
  • Top 5 Claim Submitters
  • Claims Distribution by Policy Type
  • Risk Breakdown by Flag Type

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💡 ACT

Recommendation for stakeholders:

• Investigate claimants with unusually high claim frequencies
• Review customers with high claim-to-premium ratios
• Use this dashboard as an early detection tool for fraud patterns

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🛠️ Tech Stack

Tool	Purpose
Python	Data cleaning, feature engineering, fraud logic
Pandas	Data manipulation
Tableau	Dashboard & stakeholder insights
GitHub	Version control & portfolio hosting

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🖼️ Dashboard Snapshot

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📂 File Structure

Insurance_Analytics/
├── data/
│   ├── raw/
│   │   ├── Insurance_Claims_Data.csv
│   │   └── Insurance_Customers_Data.csv
│   ├── processed/
│   │   ├── Cleaned_Merged_Insurance_Data.csv
│   │   ├── Feature_Engineered_Insurance_Data_with_ClaimID.csv
│   │   └── Final_Insurance_Claims_Data.csv
├── scripts/
│   ├── insurance_data_cleaning.py
│   ├── insurance_feature_engineering.py
│   ├── inject_fake_fraud_case.py
│   └── risk_scoring_fraud_detection.py
├── Insurance_Claims_Risk_Summary.png
└── README.md

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📣 Stakeholder Summary

This analysis examined 505 insurance claims to assess financial risk and detect potentially fraudulent activity using basic actuarial data science principles. Key findings:

• 🔍 5 claims (0.99%) were flagged as potentially fraudulent, primarily due to unusually high claim frequency or payout amounts.
• 💡 A synthetic fraud scenario was introduced to test our detection logic and validate feature engineering and risk scoring methods.
• 📊 A Tableau dashboard was created to visualize total claims, frequency patterns, and flagged risks designed to support underwriting, fraud review, or pricing teams.

📌 Business Recommendations

• Monitor claimants with high claim-to-premium ratios or abnormal frequency.
• Investigate flagged claims and refine fraud thresholds based on future data volume.
• Integrate this process into monthly claims reporting to proactively manage financial exposure.

This project demonstrates how technical data workflows can be transformed into actionable insights for business and risk stakeholders.

🧾 Script Summary Log

Script	Purpose
insurance_data_cleaning.py	Cleaned and merged customer and claims data
insurance_feature_engineering.py	Created engineered features like ratios and risk flags
inject_fake_fraud_case.py	Injected synthetic fraudulent claim for testing logic
risk_scoring_fraud_detection.py	Applied fraud and risk scoring logic to final dataset

📎 Resources

• Tableau Public Dashboard

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