An Internal Control Approach to the Construction of a Litigation Warning Model: An Application of Logistic Regression

Recently, a series of harmful financial failures such as Enron and Worldcom have led to large losses imposed on investors. Those failures suggest that financial scandals can occur with ease when internal control systems are operated deficiently. This research tries to identify which risk factors exist in the internal control system that give rise to the probability of being sued, through analyzing sued cases. 74 sued cases and 148 non-sued cases were used to develop and test a logistic regression model that estimates the likelihood of fraud-lawsuits. The significant risk factors included in the model are: management's capability, operating characteristics and financial stability, and susceptibility of assets to misappropriation. Further, the model achieved a test sample correct classification rate of 75% which is more accurate than practicing CPAs, with only 60% correct classifications. This suggests that a simple logistic model is able to identify fraud-lawsuits, and can become a useful tool for practitioners. Introduction Since 1996, a series of financial scandals such as Enron and WorldCom failures have not only surprised local and international capital markets, but also led to serious losses by investors and creditors. Suddenly, the "watch dog" was criticized to be behaved more like a lap dog (Briloff, 2001, p. 131) and attention to auditors' responsibility is greatly being drawn (AICAP 2002 , SAS No. 99). In the United States, from issuance of SAS No. 53 (AICAP 1988) clarifying auditors' obligations to detect irregularities to SAS No. 82 (AICPA 1997) requiring explicit assessment of fraud risk, level of responsibility for detecting fraud has increased, imposing a tremendous workload on auditors (Davis, 1997; Shelton et al., 2001). In response, a number of comparative studies have been largely descriptive documentations of fraud risk factors (Romney et al., 1980; Loebbecke et al., 1989) and attempted to investigate the relative importance of "red flags" or "red-flag cues" (Sorensen et al., 1982; Fanning, et al., 1995; Hansen, et al., 1996; Bell & Carcello, 2000; Apostolou, et al., 2001). However, a successful audit in fraud detection is a function of two broad performance factors: ability and professional conduct (Hansen et al., 1996; Eining et al., 1997; Catanach and Walker, 1999), both are personal and subjective so that making audit judgments in fraud detection with consistency is seldom achieved. Recent studies attempt to elicit predictability of the individual factors and guidance on how to combine such factors into an overall judgment by means of regression and intelligence technologies (Ashutosh and Tallura, 1998; Bell and Carcello, 2000; Fanning et al., 1995; Hansen et al., 1996; Green and Choi, 1997; Lenard et al., 2000). While the efficacy of those mentioned technologies to predict fraud has been tested, it is limited, in most cases, in the USA. This study extends this issue to Taiwan. Specifically, the purpose of the study is three-fold. (1) To identify which fraudulent risk factors will cause lawsuit as auditor litigation/sanction has been increased. (2) To observe the characteristics of existing internal controls of public corporations for establishing a sound corporate governance. (3) To launch an early-warning model that is applicable to examine deficiencies of internal controls so as to predict the occurrence of lawsuits resulting from fraud. The research reported in our paper develops a model to predict fraud-lawsuit; it replicates Bell and Carcello's (2000) research methodology, but extends their work in two ways. First, a "content analysis" technique was implemented on sued cases reading for selections of fraudulent risk factors. 27 factors were chosen, but with some rarely found in most cases of USA, which indicates unique risk factors rooted in the Taiwan litigation environment. second, following AICPA's emphasis on management override of internal controls, an overall understanding of internal control systems is highlighted. …