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This paper investigates Differentially Private (DP) synthetic data generation for financial institutions, addressing the tension between data utility and re-identification risks. It explores two paradigms: Direct Tabular Synthesis for reconstructing joint distributions and DP-Seeded Agent-Based Modeling (ABM) for simulating dynamic market behaviors. The results show that while tabular synthesis is suitable for static analysis, DP-Seeded ABM provides a "counterfactual laboratory" for modeling complex, stateful simulations and black swan events.
Unlock seamless cross-institutional research and compliant decision-making in finance by decoupling individual identities from data utility with differentially private synthetic data.
Financial institutions face tension between maximizing data utility and mitigating the re-identification risks inherent in traditional anonymization methods. This paper explores Differentially Private (DP) synthetic data as a robust"Privacy by Design"framework to resolve this conflict, ensuring output privacy while satisfying stringent regulatory obligations. We examine two distinct generative paradigms: Direct Tabular Synthesis, which reconstructs high-fidelity joint distributions from raw data, and DP-Seeded Agent-Based Modeling (ABM), which uses DP-protected aggregates to parameterize complex, stateful simulations. While tabular synthesis excels at reflecting static historical correlations for QA testing and business analytics, the DP-Seeded ABM offers a forward-looking"counterfactual laboratory"capable of modeling dynamic market behaviors and black swan events. By decoupling individual identities from data utility, these methodologies eliminate traditional data-clearing bottlenecks, enabling seamless cross-institutional research and compliant decision-making in an evolving regulatory landscape.