Evaluating utility in synthetic banking microdata applications

TL;DR

This paper presents a framework for evaluating the utility and privacy of synthetic banking microdata, demonstrating its application to real data from Paraguay and highlighting the effectiveness of frequency-based methods over GANs.

Contribution

It introduces a novel evaluation framework tailored for banking microdata, applied to real-world data, and compares utility of different synthetic data generation methods.

Findings

Frequency table-based applications are less affected by information loss.

Marginal inference methods outperform GANs for these applications.

Synthetic data can enhance privacy while maintaining utility for financial regulators.

Abstract

Financial regulators such as central banks collect vast amounts of data, but access to the resulting fine-grained banking microdata is severely restricted by banking secrecy laws. Recent developments have resulted in mechanisms that generate faithful synthetic data, but current evaluation frameworks lack a focus on the specific challenges of banking institutions and microdata. We develop a framework that considers the utility and privacy requirements of regulators, and apply this to financial usage indices, term deposit yield curves, and credit card transition matrices. Using the Central Bank of Paraguay's data, we provide the first implementation of synthetic banking microdata using a central bank's collected information, with the resulting synthetic datasets for all three domain applications being publicly available and featuring information not yet released in statistical disclosure. We find that applications less susceptible to post-processing information loss, which are based on frequency tables, are particularly suited for this approach, and that marginal-based inference mechanisms to outperform generative adversarial network models for these applications. Our results demonstrate that synthetic data generation is a promising privacy-enhancing technology for financial regulators seeking to complement their statistical disclosure, while highlighting the crucial role of evaluating such endeavors in terms of utility and privacy requirements.