https://github.com/tushar2704/insurance-claims-fraud-detection
The Insurance Claims Fraud Detection project is aimed at developing a robust and efficient system to identify fraudulent insurance claims
https://github.com/tushar2704/insurance-claims-fraud-detection
artificial-intelligence data-science deep-learning machine-learning python tushar2704
Last synced: 3 months ago
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The Insurance Claims Fraud Detection project is aimed at developing a robust and efficient system to identify fraudulent insurance claims
- Host: GitHub
- URL: https://github.com/tushar2704/insurance-claims-fraud-detection
- Owner: tushar2704
- License: apache-2.0
- Created: 2023-08-11T07:24:54.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-11T11:15:37.000Z (about 2 years ago)
- Last Synced: 2025-06-14T01:40:43.983Z (5 months ago)
- Topics: artificial-intelligence, data-science, deep-learning, machine-learning, python, tushar2704
- Language: Jupyter Notebook
- Homepage: https://tushar-aggarwal.com
- Size: 505 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Insurance Claims Fraud Detection
## Overview
The Insurance Claims Fraud Detection project is aimed at developing a robust and efficient system to identify fraudulent insurance claims. Fraudulent claims can cause substantial financial losses to insurance companies, affecting their credibility and increasing premiums for genuine policyholders. This project leverages machine learning techniques to automatically detect suspicious patterns and behaviors in insurance claims data, facilitating early fraud detection and prevention.
## Table of Contents
- [Project Background](#project-background)
- [Installation](#installation)
- [Usage](#usage)
- [Data](#data)
- [Features](#features)
- [Model](#model)
- [Evaluation](#evaluation)
- [Contributing](#contributing)
- [License](#license)
## Project Background
Fraudulent insurance claims are a persistent challenge in the insurance industry. Traditional manual methods of fraud detection are time-consuming, error-prone, and unable to cope with the increasing volume of data. This project aims to create an automated solution using machine learning algorithms to identify potentially fraudulent claims, thereby assisting insurance companies in saving resources and ensuring fair payouts to legitimate claimants.
## Installation
To set up the project environment, follow these steps:
1. Clone the repository: `git clone https://github.com/your-username/insurance-claims-fraud-detection.git`
2. Navigate to the project directory: `cd insurance-claims-fraud-detection`
3. Install the required dependencies: `pip install -r requirements.txt`
## Usage
Here's how you can get started with the project:
1. Prepare your insurance claims dataset (see [Data](#data)).
2. Train the fraud detection model (see [Model](#model)).
3. Evaluate the model's performance (see [Evaluation](#evaluation)).
4. Make predictions on new insurance claims data.
## Data
The success of this project relies on high-quality and diverse insurance claims data. While this repository does not include a dataset, you can use publicly available insurance claims datasets or obtain one from your organization. The dataset should include information about policyholders, claims details, and labels indicating whether a claim is fraudulent or not.
## Features
The project will include, but is not limited to, the following features:
- Data preprocessing and exploration
- Feature engineering
- Implementation of machine learning models (e.g., Random Forest, XGBoost, Neural Networks)
- Model training and tuning
- Model evaluation using appropriate metrics
- Inference and predictions on new data
## Model
The core of this project is the fraud detection model. The model will be trained on historical insurance claims data and will learn to distinguish between legitimate and fraudulent claims based on various features. The choice of algorithms and techniques will be documented in the codebase.
## Evaluation
The effectiveness of the fraud detection model will be evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The model's performance will be assessed on both training and testing datasets to ensure it generalizes well to new, unseen data.
## Installation
1. Clone the repository: `git clone https://github.com/tushar2704/Insurance-Claims-Fraud-detection.git`
2. Navigate to the project directory: `cd Insurance-Claims-Fraud-detection`
3. Install the dependencies: `pip install -r requirements.txt`
## Usage
1. Start the Flask API: `python main.py`
2. Access the web interface at your local server : `http://localhost:5000`
3. 3. Run the model on test data: `python test.py`
## Deploying and Containerizing Your Application with Docker
Before you start, make sure you have [Docker](https://www.docker.com/get-started) installed on your system.
1. **Clone the Repository:** First, clone the repository for your application to your local machine or cloud instance using the following commands:
```sh
git clone https://github.com/tushar2704/Insurance-Claims-Fraud-detection.git
cd Insurance-Claims-Fraud-detection
2.**Build the Docker Image:** Replace your-app-name with a suitable name for your application.
```
docker build -t Insurance App .
```
## To deploy on an AWS EC2 instance
- Setup an EC2 instance and SSH to the instance.Use this as a [guide](https://www.machinelearningplus.com/deployment/deploy-ml-model-aws-ec2-instance/).
- Run
```
git clone https://github.com/tushar2704/Insurance-Claims-Fraud-detection.git
```
- Start up [Docker](https://docs.docker.com) and run
```
docker build -t dockerfile .
```
- run
```
docker run -e PORT=8080 dockerfile
```
- You can now get predictions from
```
http://:8080/predict
```
## License
This project is licensed under the [MIT License](LICENSE).
## Author
- ©2023 Tushar Aggarwal. All rights reserved
- [LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)
- [Medium](https://medium.com/@tushar_aggarwal)
- [Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)
- [New Kaggle](https://www.kaggle.com/tagg27)
## Contact me!
If you have any questions, suggestions, or just want to say hello, you can reach out to us at [Tushar Aggarwal](mailto:info@tushar-aggarwal.com). We would love to hear from you!