Open AccessTeaching Case – Predicting the Probability of Company Bankruptcy with CAATs
Author(s) -
Shi-Ming Huang Shi-Ming Huang,
Yu-Ting Huang Shi-Ming Huang,
Li-Kuan Wang Yu-Ting Huang
Publication year - 2020
Publication title -
international journal of computer auditing
Language(s) - English
Resource type - Journals
eISSN - 2562-9999
pISSN - 2562-9980
DOI - 10.53106/256299802020120201002
Subject(s) - bankruptcy , debt , audit , analytics , finance , mathematics education , computer science , artificial intelligence , engineering , accounting , business , psychology , data science
The paper provides a machine-learning experimental process for a real-world corporate financial bankruptcy case: Chunghwa Picture Tubes, Ltd., in Taiwan in 2019. The teaching case addresses major topics in financial bankruptcy analytics to enable business students to learn how to analyze leveraged finance and distressed debt and to predict bankruptcy. It is a science, technology, engineering, and mathematics (STEM) teaching case with a project-based learning method. The learning goal of the teaching case is to inspire and encourage students through planned teaching activities. Students start by thinking through problems or situations and establishing a machine-learning project using computer-assisted audit technique (CAAT) software. After students conduct a self-directed project, the student can use the new knowledge to develop a new bankruptcy-case analysis.