# Evaluating utility in synthetic banking microdata applications

arXiv: https://arxiv.org/abs/2410.22519 · HTML: https://mlsanity.com/paper/2410.22519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data](https://mlsanity.com/paper/2201.12677/) (2022)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)](https://mlsanity.com/paper/2011.02352/) (2020)
- [Differential Privacy for Government Agencies -- Are We There Yet?](https://mlsanity.com/paper/2102.08847/) (2021)
- [Differentially Private n-gram Extraction](https://mlsanity.com/paper/2108.02831/) (2021)
- [Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning](https://mlsanity.com/paper/2306.15585/) (2023)
- [Confidentiality Protection in the 2020 US Census of Population and Housing](https://mlsanity.com/paper/2206.03524/) (2022)
- [Convex Total Least Squares](https://mlsanity.com/paper/1406.0189/) (2014)

## Abstract (arXiv)

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.