Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for predictive purposes, but only one recent paper provided a predictive accuracy of 80% solely by using the disclosures contained in audit reports. This study was complemented by simplifying the analysis of audit reports for prediction purposes and the same predictive accuracy was achieved. By applying three artificial intelligence techniques (PART algorithm, random forest, and support vector machine), the predictive ability of more easily extracted information from the report was examined and a practical implication suggested for each user. Simply by (1) finding the audit opinion, (2) identifying if a matter section exists, and (3) the number of comments disclosed, any user may predict a bankruptcy situation with the same accuracy as if they had scrutinized the whole report. In addition, an extended literature review is included, on previous studies on the interaction between bankruptcy prediction and the external audit information.

Recent empirical evidence shows that working in an unsupervised, isolated situation under competition, can increase dishonest behavior to achieve prestige. However, could working in a common space, in the presence of colleagues affect cheating? Here, we examine how familiar-peer influence, supervision and social incentives affect worker performance and dishonest behavior. First, we show that working in the presence of peers is an effective mechanism to constrain honest/dishonest behavior compared to an isolated work situation (experiment 1). Second, we demonstrate that the mere suspicion of dishonesty from another peer is not enough to affect individual cheating behavior (experiment 2), suggesting that reputation holds great importance in a worker’s self-image acting as a strong social incentives. Third, we show that when the suspicion of dishonesty increases with multiple peers behaving dishonestly, the desire to increase standing is sufficient to nudge individuals’ behavior back to cheating at the same levels as isolated situations (experiment 3).

Experimental studies of dishonesty usually rely on population-level analyses, which compare the distribution of claimed rewards in an unsupervised, self-administered lottery (e.g., tossing a coin) with the expected lottery statistics (e.g., 50/50 chance of winning). Here, we provide a paradigm that measures dishonesty at the individual level and identifies new dishonesty profiles with specific theoretical interpretations. We found that among dishonest participants, (a) some did not bother implementing the lottery at all, (b) some implemented but lied about the lottery outcome, and (c) some violated instructions by repeating the lottery multiple times until obtaining an outcome they felt was acceptable. These results held both in the lab and with online participants. In Experiment 1 (N = 178), the lottery was a coin toss, which permitted only a binary honest/dishonest response; Experiment 2 (N = 172) employed a six-sided-die roll, which permitted gradations in dishonesty. We replicated some previous results and also provide a new, richer classification of dishonest behavior.

Objective – The objective of this paper is to test the validity of using ‘bonus-malus’ (BM) levels to classify policyholders satisfactorily. Design/methodology/approach – In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. Findings – The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders’ policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors. Practical implications – To increase the prediction capacity of BM level, psychological questionnaires could be used to measure policyholders’ hidden characteristics. Contributions – The main contribution is that the methodology used to carry out research, the Rough Set Theory, has not been applied to this problem.

The role of financial experts is to provide their professional judgments with an opinion included in the financial reports after reviewing an entity's financial information, following a specific audit process. We investigate whether confirmation bias (through prior audit opinions) occurs among auditors during the audit process and decision-making, and whether experience mitigates this effect. A total of 175 non-experienced auditors run a 2x4 between-subjects experiment (experiment 1) studying how financial information (IV with two levels: negative and neutral/positive) and previous audit report (IV with four levels: absence, negative, moderately negative, and positive) might influence the issuance of the subsequent decision-making (DV). In addition, a total of 32 junior level 1 auditors (less than one year of experience), 31 junior level 2 auditors (up to 3 years of experience) and 20 senior auditors (more than 3 years of experience) run a 2 × 4 × 3 between-subjects experiment (experiment 2) analyzing if experience (IV with three levels of experience: less than one-year, between one and three years, more than three years) mitigates this effect (experiment 2). Results confirm that the previous-year audit report affect auditors' current assessment, showing that positive and negative prior opinions persuade auditors when suggesting the next one. This finding is relevant as auditors' opinions could be conditioned by prior opinions instead of their own expertise. Our evidence also suggests that professional experience mitigates this influence on auditors' assessments. Consequently, this study has relevant implications for partners, audit professionals and audit firm recruiters. A general implication is that auditor training courses should reinforce the auditor's own expertise and criteria based on the deep analysis of financial and economic data rather than on the work of previous auditors.