Fraud Analytics Using Predictive and Social Network Techniques

This repository offers a comprehensive overview of various analytical techniques for fraud detection and provides implementation guidance for an effective fraud prevention solution to help you detect fraud early.

Table of Contents

1. Introduction
2. Assignment
   • Cluster Identification
   • Implementation of Trust Rank
   • Example-dependent Cost-sensitive Regression
   • Fraud Detection Using Autoencoder and Variational Autoencoder
   • Synthetic Data Generation Using Variational Autoencoder
3. Setup and Installation
4. Usage
5. Contributing
6. License

Introduction

This repository is designed to explore various advanced techniques for fraud detection, including clustering algorithms, graph-based ranking methods, cost-sensitive regression models, and deep learning techniques. By utilizing these methods, you can enhance your fraud detection capabilities and effectively mitigate fraudulent activities.

Assignment

Cluster Identification

For this task, you will identify clusters within a dataset using different embedding techniques. The following methods will be implemented:

1. Node2Vec Embedding
2. Spectral Clustering
3. Graph Convolutional Networks (GCN)

These methods will help you uncover patterns and group similar instances together, facilitating the detection of anomalous behavior indicative of fraud.

Implementation of Trust Rank

Trust Rank will be implemented using the Pregel framework. Trust Rank is a graph-based ranking algorithm that helps in identifying trustworthy nodes within a network. By leveraging the Pregel framework, you can efficiently compute the Trust Rank scores for each node in a large-scale graph.

Example-dependent Cost-sensitive Regression

This task involves implementing example-dependent cost-sensitive regression based on the approach by AC Bahnsen and Nikou Gunnemann. This method adjusts the regression model to account for varying costs associated with different types of errors, improving the overall effectiveness of the fraud detection model.

Fraud Detection Using Autoencoder and Variational Autoencoder

Two deep-learning techniques will be employed for fraud detection:

1. Autoencoder: This neural network architecture is used to learn a compressed representation of the data, and any significant deviations from the reconstructed data can indicate fraud.
2. Variational Autoencoder (VAE): VAEs add a probabilistic twist to autoencoders, enabling them to generate new data samples. They can be used for both anomaly detection and synthetic data generation.

Synthetic Data Generation Using Variational Autoencoder

Variational Autoencoders (VAEs) will be utilized to generate synthetic data that mimics the properties of the original dataset. This synthetic data can be used to augment training datasets, helping to improve model robustness and performance.

Setup and Installation

To get started with the repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/iamaayushrivastava/Fraud-Analytics-Using-Predictive-and-Social-Network-Techniques.git
   cd Fraud-Analytics-Using-Predictive-and-Social-Network-Techniques

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Each task is implemented in a separate directory with detailed instructions on how to run the code. Below are the brief instructions for each task:

• Cluster Identification: Navigate to the ClusterIdentification directory and run the provided Jupyter notebooks to execute Node2Vec, Spectral Clustering, and GCN embeddings.
• Implementation of Trust Rank: Go to the TrustRank directory and follow the instructions in the README to run the Pregel-based Trust Rank implementation.
• Example-dependent Cost-sensitive Regression: Enter the CostSensitiveRegression directory and run the scripts to perform example-dependent cost-sensitive regression.
• Fraud Detection Using Autoencoder and Variational Autoencoder: Access the Autoencoders directory to find implementations and run the provided notebooks for fraud detection.
• Synthetic Data Generation Using Variational Autoencoder: In the SyntheticDataGeneration directory, follow the instructions to generate synthetic data using VAEs.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please submit a pull request or open an issue.

License

This project is licensed under the MIT License. See the LICENSE file for details.