Authors - Rishav Kumar Agrawal, Maharshi Bhowmick, Mir Abbas Hussain, Sachin, Vaishali Shinde Abstract - This paper presents a platform for scalable validation, visu alization, and explanation of synthetic tabular data in a rigorous and operationally practical workflow. The system integrates statistical test ing, dimensionality reduction, anomaly detection, and AI-assisted in terpretation into a single analysis pipeline. Through an insurance-data case study, we show that the platform can detect subtle distributional artifacts, support utility–privacy trade-off assessment, and provide in terpretable evidence that is difficult to obtain from isolated univariate checks. We conclude by discussing practical value, current limitations, and directions for future development.
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