This project explores the use of machine learning and statistical techniques to detect anomalies within a dataset of 2,500 transactions. The goal is to identify potentially fraudulent transactions by analyzing the dataset using four anomaly detection methods:
- K-Means Clustering: Detects anomalies based on distance from centroids.
- Z-Score Analysis: Flags extreme outliers in numeric features.
- Isolation Forest: Highlights anomalous transactions using tree-based partitioning.
- Logistic Regression: A supervised model used to classify transactions based on features.
The results from these methods are combined into a Fraud Risk Score, which helps prioritize transactions based on their likelihood of being fraudulent. The project focuses on providing actionable insights, with visualizations and explanations for the decisions made by the models. LIME was used to ensure interpretability, particularly for the Logistic Regression model, allowing users to understand how each feature contributes to the final decision.
- Scalable Solution: While this analysis works on a batch dataset, the approach is scalable for real-time processing. By integrating it into a cloud infrastructure, e-commerce platforms can monitor transactions continuously, detect anomalies, and respond to potential fraud in real-time.
- Flexible and Interpretable: The inclusion of geospatial data and interpretability frameworks ensures that fraud detection is not only effective but also understandable, enabling businesses to trust the system.
- Cloud Integration: The model can be integrated into a cloud-based infrastructure to handle larger datasets, monitor transactions in real-time, and enable businesses to act on fraud detection instantly.
To deploy the anomaly detection model in a real-time environment, I have proposed a simplified architecture that leverages AWS services to process transactions and help in detecting frauds. Below is the basic flow of the solution:
- Transaction Data Inflow: Transaction data (e.g., from payment providers / application checkout) is ingested in AWS Kinesis for real-time processing.
- Data Processing: An AWS Lambda function is triggered to process the incoming transaction data. It enriches the transaction with IP geolocation data using the ipinfo.io API.
- Fraud Detection Model: The enriched transaction data is sent to AWS SageMaker, where the Fraud Risk Score is computed using the pre-trained models (K-Means, Isolation Forest, Logistic Regression).
- Data Storage: The results are stored in an S3 bucket, from where they can be queried and visualized.
- Visualization: The processed results are visualized in Amazon QuickSight to generate dashboards and reports for easy interpretation.
- Alerts: Optional SNS notifications are triggered for high-risk transactions to alert business owners in real-time.
- AWS Kinesis: Ingestion of real-time transaction data.
- AWS Lambda: Serverless processing of data and model inference.
- AWS SageMaker: Running the fraud detection model.
- Amazon S3: Storing the results for further analysis and visualization.
- Amazon QuickSight: Creating dashboards to present insights from the fraud detection model.
- Amazon SNS: Sending notifications for high-risk transactions.
- This implementation is based on real-time data ingestion from e-commerce transactions and does not rely on historical batch data.
- The model is trained on a small dataset for demonstration purposes, but the architecture can scale for larger datasets and more complex models.
This README provides an overview of the project, its methodologies, and the AWS infrastructure used for real-time anomaly detection. The goal is to showcase in a simplified environment how cloud technologies, when combined with machine learning models that take into account geo-location, can address transaction anomaly detection efficiently and at any scale.