Generating synthetic transactional profiles

TL;DR

This paper presents a machine learning approach to generate synthetic transactional profiles that maintain data utility for banking insights while enhancing privacy, addressing challenges like data sparsity.

Contribution

It introduces neural network models for creating synthetic transaction data that balance utility and privacy, with an analysis of privacy-preserving techniques.

Findings

Neural networks can generate valuable synthetic transactional data.

Synthetic data preserves key banking insights.

Privacy techniques impact model performance.

Abstract

Financial institutions use clients' payment transactions in numerous banking applications. Transactions are very personal and rich in behavioural patterns, often unique to individuals, which make them equivalent to personally identifiable information in some cases. In this paper, we generate synthetic transactional profiles using machine learning techniques with the goal to preserve both data utility and privacy. A challenge we faced was to deal with sparse vectors due to the few spending categories a client uses compared to all the ones available. We measured data utility by calculating common insights used by the banking industry on both the original and the synthetic data-set. Our approach shows that neural network models can generate valuable synthetic data in such context. Finally, we tried privacy-preserving techniques and observed its effect on models' performances.