Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

TL;DR

This paper proposes a federated learning framework with privacy-preserving techniques for training deep learning models on sensitive accounting data, enabling auditors to detect anomalies across multiple clients without compromising data confidentiality.

Contribution

It introduces a novel federated learning approach incorporating differential privacy and split learning for privacy-preserving audit data analysis.

Findings

Effective anomaly detection in real-world datasets

Models benefit from multi-client data aggregation

Privacy measures mitigate data leakage risks

Abstract

The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors to examine vast volumes of a financial statement's underlying digital accounting records. As a result, audit firms also 'digitize' their analytical capabilities and invest in Deep Learning (DL), a successful sub-discipline of Machine Learning. The application of DL offers the ability to learn specialized audit models from data of multiple clients, e.g., organizations operating in the same industry or jurisdiction. In general, regulations require auditors to adhere to strict data confidentiality measures. At the same time, recent intriguing discoveries showed that large-scale DL models are vulnerable to leaking sensitive training data information. Today, it often remains unclear how audit firms can apply DL models while complying with data protection regulations. In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients. The framework encompasses Differential Privacy and Split Learning capabilities to mitigate data confidentiality risks at model inference. We evaluate our approach to detect accounting anomalies in three real-world datasets of city payments. Our results provide empirical evidence that auditors can benefit from DL models that accumulate knowledge from multiple sources of proprietary client data.