Developing and training successful machine learning (ML) fraud models requires access to large amounts of high-quality data. Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Regulation and privacy requirements further prevent data use or sharing even within an enterprise organization. The process of authorizing the use of, and access to, sensitive data often delays or derails ML projects. Alternatively, we can tackle these challenges by generating and using synthetic data.
Synthetic data describes artificially created datasets that mimic the content and patterns in the original dataset in order to address regulatory risk and compliance, time, and costs of sourcing. Synthetic data generators use the real data to learn relevant features, correlations, and patterns in order to generate required amounts of synthetic data matching the statistical qualities of the originally

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