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This chapter examines whether professional auditors’ affect toward client management influences fraud likelihood judgments and whether accountability and experience with fraud risk judgments moderate this effect. This research also explores the process by which affect influences fraud judgments by examining affect’s influence on the evaluation of fraud evidence cues. Results indicate that more positive affect toward the client results in lower fraud likelihood judgments. Accountability is found to moderate this effect, but only for experienced auditors. These findings have implications for fraud brainstorming sessions where all staff levels provide input into fraud risk assessments and because client characteristics are especially salient during these assessments. Importantly, results also support the proposition that affect impacts inexperienced auditors’ fraud assessments through errant attribution of client likability to evidence cues that refer to management, rather than biasing all client-related evaluations. Together, these findings suggest that education and training can be improved to better differentiate relevant and irrelevant cues in fraud judgment.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Previous research on auditors’ processing of nondiagnostic evidence demonstrates the existence of a dilution effect – the tendency to underreact to diagnostic information when accompanied by nondiagnostic information. Prior audit studies find that accountability, a prominent feature in audit settings, does not affect the magnitude of the dilution effect exhibited by auditors. Based on more recent theories about accountability, this line of research is extended by exploring whether (1) the dilution effect previously identified is a robust phenomenon that can be replicated, (2) accountability has an impact on both the frequency and magnitude of dilution effect, and (3) the impact of accountability on both the frequency and magnitude of dilution effect is conditional on the degree of accountability experienced by the participants through various reporting levels. The experimental results from a sample of internal auditors provide evidence supporting the first two propositions; however, the results related to reporting levels are not significant. A discussion of the implications of these findings for audit research and practice follows.

Research in psychology and accounting suggest that affect (client likeability) toward a person can impact human judgment, resulting in more favorable treatment for likeable than dislikeable individuals. This study investigates whether two debiasing mechanisms, justification and self-review, mitigate the impact of affect (client likeability) on fraud risk assessments. Consistent with prior research on nonfraud audit judgments, this study finds that in absence of any debiasing mechanism, inexperienced auditors are susceptible to affect biases in fraud judgments. Extending prior research, we find justification is not sufficient to mitigate likeability, but self-review is an effective mechanism to mitigate the effect of client likeability in a fraud judgment task. Supplemental findings indicate that general accounting experience, in itself, does not mitigate client likeability; however, the effectiveness of the self-review mechanism extends to these participants.

The current study examines the effect of fraud training on auditors' ability to identify fraud risk factors. This is important because most auditors have little or no direct experience with fraud; thus, research that investigates the potential effect of indirect experience through training is vitally important to fraud detection and audit quality. A total of 369 experienced auditors completed a complex audit simulation task that involved 15 seeded fraud risk red flags. A total of 143 auditors participated in a 30-minute training session focused specifically on fraud risk, while the remaining 226 auditors learned about general internal control risk during this time block. The results indicate that auditors with fraud training identified significantly more red flags and obtained greater knowledge about fraud risk than auditors who did not receive the training. Considering that the fraud training consumed only 30 minutes out of a 64-hour training session, the findings suggest that even modest exposure to fraud training is quite effective.

This study contributes to the cognitive processes and expertise research in judgment and decision-making in auditing. It uses the levels-of-processing theory (Craik & Lockhart, 1972) to investigate the amount of auditor attention given to information during internal control documentation procedures, and the effect of this attention on internal control information acquisition and risk assessment. Based on levels-of-processing, the attention required to complete an internal control questionnaire (ICQ) is predicted to result in the acquisition of more internal control information than when a completed ICQ is reviewed. In addition, auditors who complete an ICQ should assess control risk more like experts’ than auditors, who review an ICQ completed by another individual. Results suggest that the audit seniors who completed an ICQ retained significantly more internal control information than audit seniors who reviewed an ICQ completed by another individual. This result held when separately examining the internal control strengths and weaknesses. In addition, audit seniors who completed an ICQ-assessed control risk at a level comparable to the control risk assessments of audit managers in the same firm.

Ethics play an important role in society; however, many economics models assume that individual players act “economically” rational and ignore situations where an individual may forgo economic benefit for the public good. This chapter models the strategic interaction between auditors and management and allows for management to choose the economically irrational outcome of behaving ethically even when doing so defies their own financial self-interest. One of the model's assumption is that a certain percentage of managers do not engage in a “strategy” to misreport their financial statements because doing so is “unethical”. If recent accounting scandals are indicative of an ethical crisis in this country, this model offers hope because an increase in the percentage of unethical mangers leads to a decrease in fraudulent reporting. The model also illustrates the effects of an increase in the rewards for committing fraud (e.g., greater numbers of stock options, restricted stock, and accounting-based performance incentives) and an increase in the penalty for detected fraud (e.g., stiffer penalties for fraud from Sarbanes–Oxley).

Purpose: This chapter aims to perform text analysis to investigate the academic area delimitated by economic and financial performance and money laundering.

Need for the study: The findings contribute to the body of literature by providing important insights in terms of money laundering and financial performance.

Methodology: In order to achieve the research objective, further than 640 papers were retrieved from the Web of Science from 1994 to 2022, concentrating on the most referenced documents found in the superior quartile.

Findings: The empirical findings emphasise that the article with the unique words Fraud Detection System: A Survey by Abdallah A., Maarof M. A., and Zainal A., examines a complete and systematic assessment of the concerns and obstacles that impede the performance of fraud detection systems. Furthermore, topic modelling findings highlighted the presence of four main topics: topic 1 – identified by ‘performance’, ‘firms’, ‘financial’, ‘fraud’, and ‘board’; topic 2 – described in terms of ‘fraud’, ‘accounting’, ‘evidence’, ‘audit’, and ‘research’; topic 3 – identified by ‘firms’, ‘fraud’, ‘financial’, ‘CEO’, and ‘results’ while topic 4 – identified through ‘fraud’, ‘detection’, ‘data’, ‘cost’, and ‘card’.

Practical implications: This study will act as a guide for researchers of the financial performance field to explore the scientific publications in the field of money laudering.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.