  Exploring generative model training for synthetic tabular data, specifically
in sequential contexts such as credit card transaction data, presents
significant challenges. This paper addresses these challenges, focusing on
attaining both high fidelity to actual data and optimal utility for machine
learning tasks. We introduce five pre-processing schemas to enhance the
training of the Conditional Probabilistic Auto-Regressive Model (CPAR),
demonstrating incremental improvements in the synthetic data's fidelity and
utility. Upon achieving satisfactory fidelity levels, our attention shifts to
training fraud detection models tailored for time-series data, evaluating the
utility of the synthetic data. Our findings offer valuable insights and
practical guidelines for synthetic data practitioners in the finance sector,
transitioning from real to synthetic datasets for training purposes, and
illuminating broader methodologies for synthesizing credit card transaction
time series.
