Decision information for auditors to assess litigation risk: Application of machine learning techniques

Abstract

Fraud cases have become more common in recent years, highlighting the role of auditors’ legal liability. The competent authorities have called for stricter control and disciplinary measures for auditors, increasing auditors’ legal liability and litigation risk. This study used machine learning (ML) techniques to construct a litigation warning model for auditors to assess audit risk when they evaluate whether accept or terminate an engagement, thus improving audit quality and preventing losses due to litigation. Otherwise, a sample matching method comprised of 64 litigated companies and 128 non-litigated companies was used in this study. First, feature selection technology was used to extract six important influencing factors among the many variables affecting auditors’ litigation risk. Then a decision tree was used to establish a litigation warning model and a decision table for auditors’ reference. The results indicated that the eight outcomes provided by the decision table could effectively distinguish the level of a litigation risk with an accuracy rate of 92.708%. These results can provide useful information to aid auditors in assessing engagement decisions.

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“…It is worth mentioning that "machine learning" is the technical term that appeared most frequently in our sample. This is not surprising, since "machine learning" is an umbrella term that often includes frequently used technologies such as decision trees and artificial neural networks [76]. Deep learning is another concept that belongs to machine learning and is based on artificial neural networks [77].…”