• Journal of Information Technology Services
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• v.23 no.4
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• pp.55-70
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• 2024

In order to promote the AI applications in the financial industry, the financial sector has recently been paying attention to synthetic data technology. Synthetic data generates using a purpose-built mathematical model or algorithm, with the aim of solving a set of data science tasks. This study evaluates the utility of synthetic data by analyzing heterogeneous tabular data that is composed of discrete, categorical and continuous variables and has the feature of unbalanced data, which is commonly found in the financial sector. As a synthetic data generation technique, the TGAN and CTGAN models are applied by considering the feature of tabular data. As a result of evaluating the utility in terms of resemblance and machine learning efficiency, those of TGAN are confirmed to be high, while the quality of CTGAN are relatively poor. This is interpreted to be particularly due to the generation of categorical variables, and it suggests that how those with categorical properties especially are considered in the synthetic data generation model is a major factor in determining the utility of generation synthetic data.