A data science team at a financial institution is tasked with developing a machine learning model to detect fraudulent transactions. The team faces two major challenges: the dataset of known fraudulent transactions is extremely small, creating a severe class imbalance, and the raw data contains sensitive PII, which restricts its use due to privacy regulations. In this context, what is the most compelling rationale for generating synthetic data?
To generate a completely new dataset of hypothetical future transactions, allowing the model to anticipate novel fraud patterns that have not yet occurred.
To augment the minority class (fraudulent transactions) and create a more balanced dataset for model training, while simultaneously ensuring that no real customer PII is exposed during development.
To replace the original dataset entirely, thereby reducing data storage costs and simplifying the data ingestion pipeline for faster processing.
To perform data obfuscation on the existing PII fields, which is a regulatory requirement before any data can be used for analytics.
The correct answer is that synthetic data can be used to augment the minority class (fraudulent transactions) to create a more balanced dataset while ensuring no real PII is exposed. This directly addresses the two primary challenges mentioned in the scenario: class imbalance and data privacy. Generating synthetic data to simulate future fraud patterns is a valid but secondary use case, not the primary one for solving the immediate problem. Replacing the dataset to reduce storage costs is not a primary rationale, as the main issues are analytical (imbalance) and regulatory (privacy), and generating quality synthetic data can be complex. Using data obfuscation is a different technique from synthetic data generation; obfuscation alters existing data, while synthetic data generation creates new, artificial data points.
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