Predicting Credit Card Fraud Using Anomaly Detection

Introduction

Credit card fraud is a significant issue for financial institutions and consumers alike, with increasing volumes of online transactions making fraud detection more critical than ever. This project proposal outlines a system that leverages anomaly detection techniques to identify fraudulent transactions effectively.

Background

Anomaly detection has emerged as a promising approach for identifying unusual patterns in data that may indicate fraudulent activities. Unlike traditional methods, which often struggle with sophisticated fraud schemes, anomaly detection focuses on deviations from normal behavior. Techniques such as spectral clustering and ARIMA models have been successfully applied to detect anomalies in credit card transaction data.

Project Objective

The primary objective of this project is to develop a robust credit card fraud detection system using anomaly detection methods. The system aims to improve upon existing methods by incorporating advanced clustering techniques and leveraging large-scale transaction datasets.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the one from Kaggle for training and evaluation.
• Feature Extraction: Extract relevant features from transaction data, including transaction amount, location, time, and frequency.

2. Model Architecture

• Spectral Clustering: Implement spectral clustering to transform transaction data into a graph and analyze its spectral properties for anomaly detection.
• ARIMA Model: Use the ARIMA model for time series analysis to predict normal transaction behavior and identify deviations.

3. Training and Evaluation

• Training: Train models using historical transaction data labeled as fraudulent or legitimate.
• Evaluation Metrics: Measure performance using metrics such as precision, recall, F1-score, and area under the ROC curve (AUC).

Expected Outcomes

The proposed system is expected to achieve higher accuracy in detecting fraudulent transactions compared to traditional methods. By utilizing anomaly detection techniques, the system should effectively handle variations in transaction patterns across different users and environments.

Conclusion

This project aims to advance the field of credit card fraud detection by developing a state-of-the-art system capable of accurately identifying fraudulent activities. The integration of spectral clustering and ARIMA models is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Predicting Credit Card Fraud Using Anomaly Detection," available at ScienceDirect.

Dataset link: Kaggle Credit Card Fraud Detection Dataset