Three Essays on Information Security Breaches and Big Data Analytics: Accounting and Auditing Perspective

The dissertation examines two separate yet significant Information Technology (IT) issues: one dealing with IT risk and the other involving the adoption of IT. The IT risks that the dissertation focuses are information security breaches and the adoption/outsourcing of big data analytics. Using competitive dynamics theory and the theory of information transfer, the dissertation examines whether there is a spillover effect from information security breaches of breached firms to those firms’ rivals. Market reaction from spillover effects is captured from market activity and information asymmetry. The results suggest that the market of rival firms react to the focal firm’s experience of a data breach. However, the overall effects of data breaches on rival firms are the opposite to those to focal firms, although in many cases rival firms also experience negative reactions in the financial markets. Specifically, the results suggest that the characteristics of data breach types and previous data breach histories of focal firms have implications for rivals. However, strong information technology governance capabilities of rivals play a shielding role in mitigating those negative effects. The dissertation also examines the adoption of big data analytics by Internal Audit Function (IAF). Particularly, the dissertation examines the implications of data analytics challenges to the adoption of big data analytics by IAF. The results suggest that dataspecific IT knowledge rather than general IT knowledge is a significant predictor of adoption of big data analytics. Additionally, critical thinking skills and business knowledge also contributes to the adoption of big data analytics. Furthermore, if IAFs face management challenges, such as fraud risk detection, they are also more likely to adopt big data analytics. Results from interaction effects analysis suggest that Chief Audit Executives (CAEs) with CPA certifications are more likely to adopt big data analytics than the CAEs without CPA certification, when the size of the organization is small, when the size of the IAF is small, or when there is a lack of data-specific IT knowledge or business skills. Another important finding is that when two groups of IAFs have similar size and data-specific IT knowledge, IAFs with fraud detection responsibilities are more likely to adopt big data analytics. Finally, IAFs in Anglo culture countries are more likely to adopt big data analytics than IAFs in non-Anglo culture countries, even when both IAFs have the same size and data-specific IT knowledge. Finally, the dissertation examines the motivation of outsourcing of data analytics by IAF. The results suggest, contrary to conventional wisdom, that economic factors are not a significant predictor. Rather, strategic and sociological factors are significant in predicting the outsourcing of big data analytics. Specifically, IAFs outsource big data analytics when they lack data skills and are tasked with fraud risk management. Additionally, the role Chief Audit Executives (CAEs) is also significant. There is also a cultural variation of the outsourcing decision: IAFs from developing nations are more likely to outsource than are the IAFs from the developed countries. Further analysis of the interaction effects of these significant variables suggests that as the data skills of IAFs increase, the conditional difference of the likelihood of outsourcing decreases, suggesting that IAFs recognize both the value of data analytics and their lack of competencies. The three-way interactions of the variables support the same conclusion. The findings have implications about the formation of effective internal controls designed to mitigate the risks in the outsourcing decision. Moreover, external auditors will find the results useful when they evaluate the competence and objectivity of IAFs before they rely on their work

The role of intelligent systems in financial auditing and financial fraud

Intelligent systems have become increasingly prominent in the current competitive and changing corporate atmosphere. Although people in firms still handle many jobs, intelligent systems will become more prominent in the short/medium future and will execute everyday jobs presently executed by people considerably more effectively. Businesses must adapt and consider how human and intelligent systems skills might be combined. This study focuses on the financial auditing profession since these individuals devote a lot of time doing repetitive tasks that intelligent technologies can straightforwardly and swiftly execute. This study investigates the influence of Artificial Intelligence, Big Data, and the Internet of Things on this profession. As per the survey, financial auditors understand that intelligent systems are the way to go as a tool to help them perform their jobs, but they are still concerned to change. Employing these systems in daily financial auditing tasks is seen as having a lot of benefits by these professionals and intelligent systems professionals, but there are still some barriers to overcome. Regardless of the circumstances, intelligent systems will significantly influence financial audits

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Implications to the Audit Process of Auditing that uses Data Analytics Tools and New Business Models

Paper II is excluded from the dissertation until it is published.New advances in information technology have created a wave of technological innovations which affect the audit firms. Audit firms are now investing large sums of money to acquire and adopt data analytics tools. Using three studies in this dissertation, I investigated questions relating to the impact of digital tools in the audit process. These studies are briefly summarized below. The first study investigates whether the audit evidence from a process mining tool provides information that adds to the appropriateness (relevance) of the audit evidence collected by traditional analytical procedures. The results shows that auditors do perceive evidence from a process mining tool to express information that is relevant for both the planning and substantive stages of the audit even though the auditor’s risk assessment was higher in the substantive stage as compared to the planning stage. In addition, the results also shows that there was no difference in the auditor’s assessment of the relevance of the information presented in graph format and in written text format as both are considered equally relevant in the planning and substantive stages. The second study investigates the unintended consequences in auditor’s decision making of using digital tools with powerful visualization abilities in the audit process. Specifically, the study investigates whether auditors make their decisions based on the relevance of the information to the decision to be made when using both visual audit evidence and text evidence or their decision will be based on a bias. The results shows that when auditors are presented with different information presented in different formats (visual or text), they are most likely to use the piece of information presented in visual rather than using the piece of audit evidence which is relevant to the decision. The third paper analyses the fraud case of a financial technology company Wirecard using the fraud triangle as the theoretical framework. The results shows that of the three factors identified in the fraud triangle, opportunity was the most prevalent factor and rationalization was least observable.publishedVersio

Do Fraudulent Companies Employ Different Linguistic Features in Their Annual Reports? An Empirical Study Using Logistic Regression and Random Forest Methodologies

The use of textual analysis to uncover fraudulent actions in 10-K filings is widespread. The previous studies have looked at the Management Disclosure and Analysis (MD&A) section of annual reports to predict illicit behaviour by analysing the tone of executives, with the majority of those studies dating back 10 years or more. The primary goal of this research is to find patterns in linguistic features of entire annual reports of convicted public businesses, which were found using the Corporate Prosecution Registry database, and compare them to non-fraudulent equivalents in the same industry. The algorithms of logistic regression and random forest are implemented to discover important factors and make accurate predictions. The accuracy rate, ROC-AUC value, and 10-fold cross-validation tools are performed to validate the success of each method. The results of the logistic regression revealed that corrupt organisations utilise a more negative, uncertain, and litigious tone. Furthermore, these businesses employ more words with a high lexical diversity and minimal complexity. Based on the Random Forest machine learning technique, the litigious variable is the most important variable in the prediction of untruthful corporations. Moreover, each of the validation methods demonstrates that the Random Forest methodology outperforms logistic regression.nhhma

TRENDS ON REPORTING MATERIALITY INFORMATION IN THE INDEPENDENT AUDITOR’S REPORT – CASE OF CROATIA

It is known that the financial statement audit represents the corporate governance mechanism crucial for ensuring the appropriate quality of the financial reporting process and financial statements. One of the most significant aspects of the financial statement audit process is the application of the materiality concept. Auditors apply the concept in planning and performing the process, as well as in evaluating the effects of identified misstatements. The International Accounting Standards Board (IASB) defines that the information provided in financial statements is material if could reasonably be expected that will influence the business decisions of the stakeholders. Although not mandatory, recent Standards and regulation changes resulted in reporting materiality details by a significant number of auditors in Croatia. The research question is how that practice develops from the implementation year, 2016, to nowadays, 2020, and what can be expected in the future. Following the research problem, the objective of the paper will be to investigate the current state and future perspective of disclosing information regarding materiality in the independent auditor’s report in Croatia. To investigate the research problem, we analyzed independent auditor’s reports of Croatian listed companies (public interest entities - PIEs) from 2016 to 2019. The research is conducted by applying appropriate statistical methodology as descriptive statistics, cluster analysis, and non-parametric tests, and regression analysis

From Conventional Methods to Contemporary Neural Network Approaches:Financial Fraud Detection

This chapter provides insights on the underlying reasons to replace the conventional methods with contemporary approaches—the neural network-based machine learning methods—in financial fraud detection. To do this, we perform a systematic literature review on the evolution of financial fraud detection literature over the years from traditional techniques toward more advanced approaches such as modern machine learning methods like artificial neural networks. Additionally, this chapter provides concise chronological progress of the fraud literature and country-specific fraud-related regulations to draw a better framework and give the idea behind the corpus. Using the metadata in the existing literature, we show both benefits and costs of using machine learning-based methods in financial fraud detection. An accurate prediction using contemporary approaches is essential to minimize the potential costs of fraudulent financial activities for stakeholders, reduce the adverse effects of fraudsters’ and companies’ fraudulent activities, and increase trust in capital markets via continuous fraud risk assessment of companies