How This Workflow Works
This workflow demonstrates several outlier detection methods to identify fraudulent credit card transactions. It partitions and normalizes the data, applies seven different detection techniques, and evaluates each method's performance using recall and precision on a common test set.
Key Features:
- Compare multiple fraud detection techniques side by side
- Evaluate model performance using precision and recall, taking into account class imbalance
- Automate threshold and parameter optimization for each method
- Visualize comparative results for informed decision-making
Step-by-step:
1. Apply Outlier and Anomaly Detection Methods:
The workflow uses a range of techniques—including statistical, clustering, and machine learning approaches—to identify transactions that deviate from typical patterns. These include quartile-based, distribution-based, clustering (DBSCAN), Isolation Forest, Autoencoder, Logistic Regression, and Random Forest methods.
2. Optimize Detection Thresholds and Hyperparameters:
Where applicable, the workflow automatically optimizes key hyperparameters (such as thresholds for outlier scores, or the maximum distance for points to count as neighbors in clustering) to maximize detection performance. This ensures that each technique is fairly evaluated and operates at its best for the given data.
3. Evaluate and Compare Model Performance:
After applying each detection method, the workflow calculates precision and recall metrics on the same test set. This allows for a direct comparison of how well each technique identifies fraudulent transactions, especially considering the dataset's imbalance.
4. Visualize and Share Insights:
The workflow compiles the performance metrics from all techniques and presents them in a comparative bar chart. This visualization helps stakeholders quickly understand which methods are most effective for fraud detection in this context.