Vehicle Insurance Claim Fraud Detection

Project Description

According to the Insurance Information Institute (III), insurance fraud is a deception committed against an insurance company for financial gain. It can be anything from lying about a garaging address (the location where your vehicle is parked for most of the year) to exaggerating an accident to outright staging one. Fraudulent claims are extremely difficult to detect causing immense losses on the part of the insurance company.

This project delves into the application of clustering as a tool for anomaly detection in the insurance industry, aiming to address and mitigate the impact of such deceptive practices.

Project Coverage

Data Download
Data Pre-Processing
Exploratory Data Analysis
[Feature Engineering]
[Modelling]

Model

[Enumerate model used here]

Data Source

https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection

Setup

Install all requirements by running the following command

pip install -r requirements.txt

Project Configurations

Hyperparameter Tuning: insert here
Pipeline: insert here
Model Tracking: insert here
Deployment: insert here

Project Folder Structure

├── ...
├── 01_src  				# Source codes
│   ├── data
│   │   ├── 01_download_data.py
│   │   └── 02_data_processing.ipynb
│   ├── features
│   ├── models
│   └── visualization
├── 02_data
│   ├── 01_raw  			# Raw data files
│   │   └── fraud_oracle.csv
│   ├── 02_processed 			# Processed data files
│   │   └── fraud_oracle_processed.csv
│   └── 03_external  			# Data from external sources
├── 03_notebooks  			# Notebooks used for pre-processing, exploration, model training, etc
│   └── 01_exploratory_data_analysis.ipynb
├── 04_models  				# Trained model files, model metadata, and evaluation results
├── 05_deployment  			# Project deployment files
├── 06_reports  			# Project documentation, Jupyter Notebook reports, final reports, and presentations
├── 07_config  				# Configuration files for hyperparameters, data sources, logging, environment, database, and deployment
├── 08_tests 				# Unit tests or test scripts
├── 09_environments 		        # Environment setup file (dependencies)
├── README.md
└── ...

Pull Requests

If you have something to add or a new idea to implement, you are welcome to create a pull request on improvement.

References

• [Add references here](insert link here)

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