A robust machine learning project to detect fraudulent insurance claims using Python (EDA, feature engineering, model development) and Power BI (business dashboard).
The solution balances recall (fraud detection coverage) and precision (investigation efficiency) while providing interpretable results for business stakeholders.
Fraudulent claims cause significant financial losses for insurers.
This project demonstrates how machine learning can help identify high-risk claims, reduce losses, and provide transparent explanations for fraud decisions.
Key components:
- Exploratory Data Analysis (EDA) – cleaning, binning, handling leakage.
- Modeling – Logistic Regression, Random Forest, and tuned XGBoost with SMOTE for class imbalance.
- Threshold Optimization – F2/F3 score and precision-recall trade-offs.
- Interpretability – Global & Local SHAP explanations.
- Business Dashboard – One-page Power BI dashboard with KPIs, trends, fraud by category, and top fraud drivers.
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Machine Learning
- Handles severe class imbalance with SMOTE.
- Hyperparameter tuning with Optuna.
- Evaluation using ROC-AUC, PR-AUC, precision, recall, F2/F3.
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Explainability
- Global SHAP (top contributing fraud drivers).
- Local SHAP (top reasons per claim flagged as fraud).
- Aggregated outputs for business users.
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Power BI Dashboard
- KPIs: Fraud Rate %, Fraud Detected, Investigation Efficiency %, Fraud Detection Coverage %.
- Fraud trends over time.
- Fraud by categories (vehicle usage, region, etc.).
- Top fraud drivers (interpretable).
- Per-claim fraud explanations.
. ├── notebooks/
│ └── Insurance_Fraud_Detection.ipynb
├── data/
│ ├── FactClaims.csv
│ ├── FactModelScores.csv
│ ├── FactLocalReasons.csv
│ ├── FactLocalReasonsAgg.csv
│ ├── DimFeature.csv
│ └── DimDate.csv
├── r/
│ └── Load and Export Dataset from R.R
├── dashboard/
│ └── Insurance_Fraud_Dashboard.pbix
├── src/
├── requirements.txt
└── README.md
This project uses the freMPL6 insurance claims dataset, available through the CASdatasets package in R.
We provide two options:
Option A — Quick Start (use the bundled CSV) We’ve already exported the dataset for you: data/raw/freMPL6.csv. Load it directly in Python with: import pandas as pd df_raw = pd.read_csv("data/raw/freMPL6.csv")
Option B — Reproduce the export with R (full reproducibility) Use the provided R script: r/Load and Export Dataset from R.R. Run it from the repo root: Rscript "r/Load and Export Dataset from R.R"
References Agresti, Alan. 2013. Categorical Data Analysis, 3rd Edition. Charpentier, Arthur. 2014. Computational Actuarial Science with R. The R Series. Chapman & Hall/CRC.
Link Source Project and Author Frequency analysis with a Zero Inflated Regression of a French Motor Third Party Liability dataset Author: Siharath Julien Published: October 1, 2024 Source Link (file:///C:/Users/Source/OneDrive%20-%20IESEG/Documents/Data%20Projects/Insurance%20Fraud%20Detection/Frequency%20analysis%20with%20a%20Zero%20Inflated%20Regression%20of%20a%20French%20Motor%20Third%20Party%20Liability%20dataset.htm)
Clone the repo:
git clone https://github.com/your-username/insurance-fraud-detection.git cd insurance-fraud-detection
Create a virtual environment and install dependencies: python -m venv venv source venv/bin/activate # or venv\Scripts\activate on Windows pip install -r requirements.txt
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Data Preparation & Modeling
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Run the Jupyter notebook: jupyter notebook notebooks/Insurance_Fraud_Detection.ipynb
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Export Model Outputs for Power BI The notebook generates CSVs: FactClaims FactModelScores FactLocalReasons FactLocalReasonsAgg DimFeature DimDate
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Dashboard (Published and Public) Open Insurance_Fraud_Dashboard.pbix in Power BI Desktop. Or view the published dashboard here:
- Insurance Fraud Detection Dashboard: Live Dashboard Link