Core Concepts
Synthetic data applications in finance address privacy, fairness, and robustness concerns while enhancing decision-making processes.
Abstract
The article delves into the applications of synthetic data in finance, focusing on tabular data generation, privacy considerations, fairness implications, and model robustness. It explores various generative models like CTGAN and CopulaGAN, evaluates their utility in fraud detection scenarios using AUROC metrics, discusses differential privacy approaches for synthetic data generation, and examines the trade-offs between privacy protection and utility. The discussion extends to fairness considerations and the impact of synthetic data on model robustness.
Directory:
Introduction
Background and Related Work
Data Liberation Modalities Models Applications
Augmentation Counterfactual Scenarios Testing
Synthetic Data Generation with Python Libraries Criteria Comparison
Privacy Risks Regulations Defenses Levels Credit Card Fraud Use Case Evaluation
Fairness Analysis
Model Robustness Exploration
Stats
Synthetic data is utilized for fraud detection with an imbalanced credit card dataset.
Various generative models like CTGAN and CopulaGAN synthesize imbalanced datasets.
Differential privacy techniques are employed for privacy-preserving synthetic data generation.
DP-MERF outperforms a space partitioning-based algorithm in terms of ROC values.
SC-GOAT approach excels in generating optimal synthetic data mixtures for fraud detection.
Quotes
"Various metrics are utilized in evaluating the quality of our approaches in these applications."
"Synthetic data can help robustify our training samples when the generated samples are sufficiently diverse from the original dataset."