Modeling financial time series by stochastic processes is a challenging task and a central
area of research in financial mathematics. As an alternative, we introduce Quant GANs, a …

This paper proposes the concept of artificial intelligence co-piloted auditing, emphasizing
the collaborative potential of auditors and foundation models in the auditing domain. The …

Auditing has to adapt to the growing amounts of data caused by digital transformation. One
approach to address this and to test the full audit data population is to apply rules to the …

Bookkeeping data free of fraud and errors are a cornerstone of legitimate business
operations. The highly complex and laborious work of financial auditors calls for finding new …

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have
the ability to address competitive resource allocation problems together with detection and …

Over the past decade, Artificial Intelligence (AI) technologies have quickly become
implemented in protecting data, including detecting fraud in healthcare organizations. This …

The ongoing 'digital transformation'fundamentally changes audit evidence's nature,
recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires …

Nowadays, organizations collect vast quantities of accounting relevant transactions, referred
to as' journal entries', in'Enterprise Resource Planning'(ERP) systems. The aggregation of …

In this study, we propose a novel autoencoder framework based on orthogonal projection
constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets …

International audit standards require the direct assessment of a financial statement's
underlying accounting transactions, referred to as journal entries. Recently, driven by the …