Synthetic Data for Counterfactual Targeting in Regulated Industries

Abstract

Privacy constraints, data sparsity, and ethical considerations limit focused actions using consumer data in regulated businesses including healthcare, banking, and insurance.  This study examines counterfactual targeting, which replicates “what-if” situations to evaluate policy or marketing decisions at the person level using synthetic data.  Organisations may predict alternative outcomes without compromising user privacy or compliance by creating high-fidelity synthetic datasets that maintain statistical features and causal linkages of real-world data.  The study uses synthetic data creation and causal inference algorithms to assess treatment effects across varied populations.  Case studies show how this strategy promotes strategic decision-making while protecting data.  The findings show that synthetic data may be used to innovate predictive modelling and individualized decision-making in high-stakes, regulated situations while protecting privacy.