Soohyun Cho

AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI

Chae-Gyun Lim, Seung-Ho Han, Jeongyun Han, Soohyun Cho et autres

The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks in non-English, socio-cultural contexts such as Korean, and are often limited to the text modality. To address this gap, we …

Predicting Material Misstatements Using Machine Learning

Chanyuan Zhang, Lanxin Jiang, Soohyun Cho, Miklos A. Vasarhelyi

ABSTRACT This study uses machine learning models to forecast future material misstatements. Using raw financial data, audit variables, qualitative features, and an efficient algorithm, we design a dynamic model that continuously updates with new information. Our model outperforms the benchmarks for both …

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Examining the Effectiveness and Efficiency of Full Population Testing versus Traditional Sampling

Feiqi Huang, Soohyun Cho, Kyungha Lee, Won Gyun No

SYNOPSIS Full population testing (FPT) has been proposed as an alternative to traditional sampling, enabling auditors to manage a potentially large number of items leveraging advances in data analytics. This study applies FPT to substantive testing in an audit using purchase transaction …

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Outlier Detection in Auditing: Integrating Unsupervised Learning within a Multilevel Framework for General Ledger Analysis

Danyang Wei, Soohyun Cho, Miklos A. Vasarhelyi, Liam Te-Wierik

ABSTRACT Auditors traditionally use sampling techniques to examine general ledger (GL) data, which suffer from sampling risks. Hence, recent research proposes full-population testing techniques, such as suspicion scoring, which rely on auditors’ judgment to recognize possible risk factors and develop corresponding risk …

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