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Initial Picture of the Topic
In order to successfully investigate instances of financial fraud, forensic accounting specialists have to possess a diverse set of skills and knowledge in the fields of finance, auditing, investigations and criminology, psychology, accounting information systems, communication, and law. Additionally, information technology is an integral part of forensic accounting in the contemporary world (Crain, Hopwood, Pacini & Young 2015). The topic of innovative technology and its connection to forensic accounting and auditing is essential because it explores the relatively new aspect of financial management, auditing and crime investigation – artificial intelligence (AI) and machine learning (ML), which can have a significant impact on the domain of forensic accounting as the volume of produced and collected information is continuously increasing, making it difficult for individuals or even teams to analyze and detect patterns. There are two aspects of this technology that should be considered, the detection of abnormalities that can indicate fraudulent activity and the ability to locate hidden fraud. The latter implies an automated detection of scenarios that can be fraudulent.
Hence, one can argue that in the following years, the use of AI and ML will become an integral part of forensic accounting, which highlights the need for research on best practices and government policies regulating this element. This initial topic review argues for the need to develop a cohesive practice that can be used by the government of Saudi Arabia to ensure that different cases of financial fraud are detected at early stages. For instance, some researchers in the field introduced specific machine learning algorithms that can help accountants detect financial fraud.
Overview of the Research
Forensic accounting is an essential element of a country’s financial security, and the development of technology that allows for the enhancement of analysis is vital in the contemporary world. This research fills the gap in the existing literature by presenting a comprehensive analysis of the strategies that can be used by governments to detect fraudulent activities using the example of Saudi Arabia’s Vision 2030 project.
Research Problem
This research aims to investigate the prospects of using Big Data, Al, and ML in forensic accounting. The research will make an original contribution to the accounting literature because it will develop practical advice based on the example of Saudi Arabia that governments or private companies will be able to apply when designing practices for detecting fraud and money laundering. The existing body of knowledge on the topic is limited to the use of forensic accounting in general or within an industry while this proposal highlights the need for developing governmental policies. Additionally, the priorities of Alliance MBS include fostering research practices that impact businesses and policymakers, and this proposal outlines the need to address a possible gap in literature relating to government-induced policies regarding the development and use of technology in investigating financial crimes (Our vision n.d.). The emphasis of MBS on social responsibility correlates with the purpose of this proposal – develop a strategy that would help eliminate the risk of collapse within large corporations that will inevitably affect the employees and a country’s economy.
Hypothesis
The hypothesis is that technological advancements will help significantly improve the Saudi Arabia government’s ability to detect financial fraud. From a theoretical perspective, the intersection between government policies and strategies and the necessity for using technology is not discussed thoroughly in the professional literature. In light of this, this proposal aims to investigate the examine the implication of financial fraud, forensic accounting strategies that can be applied by the government of Saudi Arabia to combat the issue for its Vision 2030 project successfully.
Objectives
The primary aim is to examine the implications of using big data and machine learning algorithms for overcoming this problem and detecting unlawful financial operations in the early stages. The primary objective of this research is to develop a policy for Saudi Arabia’s government that will help integrate Big Data, AI, and ML into forensic accounting. This will be achieved through the review and assessment of the current literature and evaluation of the computer algorithms that can be used by governments.
The Positioning of the Research
The focus of this proposal is to examine the scope of forensic accounting in light of new technology and determine policies and practices that Saudi Arabia can use to mitigate risks of money laundering during the implementation of its Vision 2030 project. Some key terms relating to innovative technology that will be explored in this paper are artificial intelligence (AI), which is a use of computer algorithms to mimic the cognitive processes of humans, and machine learning (ML) that allows the algorithm to develop and overcome areas of uncertainty. The recent publications, such as articles by Vasarhelyi, Kogan, and Tuttle (2015) and Appelbaum, Kogan, and Vasarhelyi (2017) on the prospects of big data in accounting suggest the need to develop frameworks and invest in organizational capabilities of collecting and storing information about financial and related non-financial operations and events in order to further analyze it. Moreover, the evidence and arguments presented by the authors suggest that big data is an inevitable aspect of evolution in current practices and approaches to accounting that will inevitably impact this field.
The most crucial scholarly literature discussing the application of artificial intelligence in accounting is the textbook explaining the specifics of forensic accounting by Crain, Hopwood, Pacini, and Young (2015) that outlines the principles and current best practices of accounting crime investigation and auditing and bridges the gap between the understanding of information technology and its application in financial monitoring. The evidence suggests that although most organizations and governments do not use AI or ML as part of forensic accounting, the field already utilizes a large number of information technology benefits for collecting and analyzing information. Further advancement of this practice can only improve the accuracy of money laundry detection. Next, articles by Sutton, Holt, and Arnold (2016) and Kokina and Davenport (2017) discuss the implications of AI and automation for accounting in general. Although the focus is mostly on accounting and auditing, these articles present cases of large companies implementing AI to enhance the process of analyzing financial information, which also has implications for forensic practices.
Machine learning (ML) is a subpart of AI, with a substantial improvement that allows the algorithms to create new patterns of analysis when it encounters an obstacle or an unknown. According to Flood, Jagadish, and Raschid (2016, p. 129) “exponential growth of machine-readable data to record and communicate activities throughout the financial system has significant implications for macro-prudential monitoring.” One issue is the ability to scale a machine learning algorithm that will be able to analyze substantial volumes of data generated by financial institutions. The machine learning algorithms applied to detect fraud in finance are becoming more and more critical in the contemporary world.
Forensic accounting allows investigating fraudulent activities using analysis of financial information. In the contemporary world, the emergence of new technologies provides both benefits for such investigations, as well as difficulties because new ways of conducting financial fraud emerge. The financial industry globally is suffering from various forms of fraud that result in substantial losses for companies and individuals. Accounting fraud is a substantial issue in contemporary society because it leads to the destruction of impairment of adequate functioning of the financial systems and affects large corporations, resulting in collapses (Jofre, 2017). However, the detection and investigation of such issues are usually obstructed by senior executives and managers, making it difficult to detect and investigate these financial operations.
New technology such as big data analysis and machine learning provides a potential for mitigating this problem and detecting patterns associated with financial fraud. This aspect is especially crucial in light of Saudi Arabia’s Vision 2030 program that aims to invest in the development of the countries’ industries. Jofre (2017, p. 10) defines financial crimes as “calculated misrepresentation of the financial statement information disclosed by a company in order to mislead stakeholders regarding the firm’s true financial position.” This can be facilitated through either direct manipulation of accounting records or through the development of strategies that allow receiving financial gain.
From the perspective of a county, being able to prevent or detect fraud in the early stages is crucial for mitigating the risks for the entire financial system. In recent years, financial institutions and researchers report an increase in the number of cases associated with financial fraud (Jofre 2017). The most common cause of financial fraud investigated by forensic accounting is an inaccurate representation of finances that deceives the main stakeholders. According to Jofre (2017, p. 2), “exposed on October 2001, this scam concluded with the bankruptcy of the company, followed by 4,500 employees who lost their jobs and pension funds, and an estimated loss of 74 billion dollars assumed by investors and stakeholders.” Inarguably, this presents a significant threat to a county’s economy. Nilson’s report argues that over 22.8 billion US dollars caused within a year due to fraud with credit cards and the number is expected to grow by 2021 (cited in Jofre 2017).
Forensic accounting is connected to the theory of the fraud triangle, which explains why a person commits a crime, pertaining to the motivations and serving as a framework that can be used to design strategies for detecting and preventing fraud (Huber 2017). The perception of forensic accounting may differ, depending on the country and specific laws that guide financial operations. Hegazy, Sangster, and Kotb (2017) argue that this tool is essential for contemporary crime investigation as the nature of fraudulent activity has changed, evolving alongside the innovative technology and the emergence of new payment models or monetary institutions.
One example of new detection strategies is blockchain, the most famous example of which is bitcoin. Despite some limitations, “blockchain systems are very effective in preventing objective information fraud, such as loan application fraud, where fraudulent information is fact-based” (Cai & Zhu 2016, p. Hence, the technology used in forensic accounting can be reviewed from a variety perspectives, since a large number of strategies for monitoring and detection, for instance, big data analysis, AI, ML, or blockchain exist.
Big Data
The application of Big Data in accounting is a recent development, and due to this reason, no specific governmental policies regulating this technology currently exist. However, it possesses the three main advantages distinguishing it from the commonly used accounting methods – volume, velocity, and variety. Kharel and Titera (2015) argue that the existing accounting standards are not compatible with the volumes of information produced by companies, nor do they outline any specifics of managing big data in finance. From the perspective of forensic accounting, this may become an issue, since, without tools and information technology skills, investigators will be unable to perfume their work adequately.
The Big Data challenge arises as a problem in regards to auditing as well for similar reasons. Without an infrastructure for collecting, standardizing, and analyzing financial information, auditors will be unable to perform their work. Moreover, Worren, Moffitt, and Byrnes (2015, p. 397) predict the emergence of new types of information that accountants will be able to use in their work, since “the video, audio, and textual information made available via Big Data can provide for improved managerial accounting, financial accounting, and financial reporting practices.” Hence, this project will help the government of Saudi Arabia by developing a new policy that outlines the use of financial big data and the specifics of its analysis in the context of forensic accounting since currently, a practice gap in this area exists.
Auditing
Auditing is another aspect of accounting and accounting forensics is subjected to changes due to the evolvement of technology. According to Forbes’ (2017), 2025 report on auditing suggests that most professionals in the field anticipate a substantial increase of the analyzed sample sizes, which will require an application of technology for the daily work of an auditor. The main emphasis is on technologies that mimic the cognitive functions of humans – AI is able to perform analyzes and detect patterns similar to the work of an auditor but on a larger scale.
One approach that illustrates the benefits of new technology in auditing is the ability to collect and compare structured and unstructured data, such as news, social media posts, internet articles in comparison to the client’s financial records.
The theoretical gaps that exist are a lack of evidence-based best practices that would help businesses or governments apply big data analysis in auditing, using both structured and unstructured data to produce reports of high accuracy. This approach will allow auditors to consider a large number of elements affecting a company. From a practical perspective, Popoola, Rayaan, Samsudin, and Ahmad (2016a) outline the practice of detecting money laundering in Saudi Arabia’s banking sector, but the work lacks the recognition of innovation that will inevitably affect many industries in the country. Moreover, Vision 2030 will foster the establishment and growth of a large number of new companies, resulting in a necessity for analyzing large quantities of data.
Vision 2030
This proposal aims to outline the primary efforts of Saudi Arabia in an attempt to combat financial fraud through forensic accounting and reach the objectives of its vision 2030 initiative. The vision 2030 initiative in the Kingdom of Saudi Arabia is a strategy that outlines the plan of this state to diversify the economy. One aspect of this is becoming less dependent on the oil industry and investing in other sectors of the economy, such as healthcare and education. The financial aspect of this initiative is connected to the Financial Sector Development initiative, which will benefit from the introduction of new technologies such as machine learning. The publication that help enhance one’s understanding of the Vision 2030 project is a study by Nurunnabi (2017) argues that the country was able to achieve significant growth and remarkable economic results by effectively using its oil resources. However, in order to create a sustainable economy, the government of Saudi Arabia will have to develop and introduce policies that address the main issues, including information technology and economic transformation. This is the main scope of this research since the primary objective is to define strategies for leveraging technology to improve the financial practices within the state.
The gap in the existing articles is the lack of recognition relating to the government-supported strategies for monitoring the financial activities of the new companies, which is necessary since one can anticipate that a large number of corporations will be established under this program. Hence, this proposal suggests that countries such as Saudi Arabia that aim to promote the development of new businesses and industries should also invest in the establishment of frameworks and capabilities allowing forensic accountants to prevent or locate fraud to ensure that no significant financial harm is executed.
Research Design and Methodology
The primary implication of this research is the analysis of best practices that can be used by institutions to execute efficient forensic accounting and auditing practices using innovation. Hence, the focus of the methodology will be on analyzing current publications, statistics, and country-specific data, since the proposal focuses on Saudi Arabia and more specifically on its Vision 2030. For this research, the methodology is a mixed design study, employing both quantitative and qualitative analysis to collect evidence and best practices relating to the issue of forensic accounting.
The quantitative and qualitative data that describes the current issues in the financial monitoring within Saudi Arabia, the state of accounting forensics in the country, and issues that can arise as a result of implementing the vision 2030 project will be collected. Additionally, information about new AL and ML algorithms used in accounting and auditing will be collected and evaluated. This information will be collected by accessing the scholarly publications and accounting journals that can be obtained through the library. In most cases, accounting journals providing peer-reviewed articles on the topic discussed in this research can be accessed online. Hence, no difficulties are anticipated during the process of locating sources and evaluating them.
It is evident that in order to make valid conclusions and present a cohesive policy proposal, the collected data and evidence should be supported by real-life examples. Hence, the studies and publications that present execution examples, for instance, fraud detection algorithms that helped detect money laundry practice, will be chosen for this research. Bllomfield, Nelson, and Solters (2016) suggest using a framework developed by the author to collect accounting-specific data efficiently, emphasizing the difficulty associated with obtaining valid information. The main criteria that will be used to evaluate the journals are the relevance to the topic, data of publication since it is vital to incorporate the latest advancements in AI and ML into this research, and the practical implications that will allow applying the proposed algorithms or strategies in practice.
Some difficulties may arise when looking for data regarding fraudulent activities and methodology used to detect crime since this information either belongs to private companies unwilling to disclose it or to state institutions regulated by specific laws. However, the examination of case studies, such as the one by Popoola et al. (2016b), in which the authors refer to the frequent corrupt practices in Saudi Arabia banks that require intervention from policymakers, can be used for qualitative assessment. Hence, similar publications on the topic can provide an understanding of the issue. According to Vatters “one of the real limitations of empirical research is that we tend to work on problems we are able to study because data are available; we thereby tend to overlook problems that we ought to study, if data for such problems are not easy to obtain” (cited in Bloomfield 2016). It is recognized that some data relevant to the issue will be impossible to gather due to security implications. However, this proposal and investigation aim to investigate a novel topic.
Another strategy for overcoming the issue with data collection is a partnership with a company or a group of researchers that work in the same field and develop strategies for detecting money laundering. However, currently, no implications for such an approach exist. The main difficulty, however, is connected to the ability to access and evaluate information about the Vision 2030 project. In order to understand the effect and potential dangers that can affect the state of financial operations in the country, it is vital to be able to examine the specifics data explaining the investment strategies and approaches that the government of Saudi Arabia is already taking to improve the auditing practices and accounting investigations. Some information on the issue is available in open access, such as the publication by Nurunnabi (2017) who describes the specifics of the program and the steps that the government plans on taking. Since the main issue explored by the author and defined by this proposal is the problems connected to the transformation of the oil-centered economy towards a knowledge-based economy, other scholarly publications exploring the same topic will be reviewed as well.
Since the topic requires an in-depth review of the Vision 2030 program, its implications, and the impact of auditing strategies and forensic accounting, the proposed method of research is a structured literature review (SLR). According to Massaro, Dumay, and Guthrie (2016), SLR in accounting can be carried out through a combination of quantitative and qualitative methods. The primary goal of SLR is to examine the publications on the topic, identify the main trends and specifics of applying AL and ML in forensic accounting. Next, insights can be developed based on these findings that will help develop a government-supported approach that Saudi Arabia will be able to use to detect financial crimes. This research combines both forensic accounting, which implies an investigation of financial operations to detect fraud, and auditing, which is more focused on presenting an unbiased examination of a company’s financial statements because both processes can be significantly improved using the technology in question.
More specifically, by using big data, investigators will be able to automate the process of reviewing vast quantities of financial records. The overall goal of this research is to determine the validity of the chosen approach and identify whether the government of Saudi Arabia will be able to use this novel technology to ensure the safety of financial operations within the country. SLR will help locate different views and empirical evidence or case studies describing similar cases, which will help develop a cohesive understanding of the benefits that forensic accounting can receive from AI, ML, and big data.
Access to online libraries significantly improves the ability to analyze scholarly information. As stated by Massaro, Dumay, and Guthrie (2016), the internet has transformed the ways in which scholars communicate with each other and the overall ability of people to search and read scholarly publications. One issue with this is the overwhelming quantity of information, in this case, a large number of papers published on the topics of forensic accounting and auditing and the application of innovation in this field. Hence, it is vital to develop a strategy for evaluating these sources and choosing only relevant ones that will help create a policy for Saudi Arabia. The sources will be evaluated based on their credibility, and primary sources on the topic will be used. This will include statements from the Saudi Arabia government and scholarly research. The authors’ credentials will be examined to include scholars with prominent work in the field of forensic accounting.
The purpose of the analysis is to review the practical application of big data, AL, and ML in the context of Saudi Arabia’s transformation. To analyze data the SLR method, which involves a comprehensive and in-depth review of forensic accounting, auditing, innovation, and applicability of computer algorithms to the enhancement of Saudi Arabia’s efforts for diversifying its economy by 2030 in accordance with its Vision 2030 program will be used. The articles, policies, and studies will be collected and analyzed using a qualitative approach, such as a small n test to examine patterns of application within the innovation in question. Additionally, unlike traditional systemic or structured reviews, SRL methodology provides more freedom for the researcher, which is necessary able to evaluate the various aspects of forensic accounting.
Proposed Timetable of Study
Reference List
Appelbaum, D, Kogan, A & Vasarhelyi, MA 2017, ‘Big Data and analytics in the modern audit engagement: research needs,’ Auditing: A Journal of Practice & Theory, vol. 36, no. 4, pp. 1-27.
Bloomfiled, R, Nelson, MW & Solters, E 2016, ‘Gathering data for archival, field, survey, and experimental accounting research’, Journal of Accounting Research, vol. 54, no. 2, pp. 341-395.
Cai, Y & Zhu, D 2016, ‘Fraud detections for online businesses: a perspective from blockchain technology’, Financial Innovation, vol. 2, no. 1, pp. 1-10.
Crain, MA, Hopwood, WS, Pacini, C & Young, GR 2015, Essentials of forensic accounting, American Institute of Certified Public Accountants, New York.
Flood, MD, Jagadish, HV & Raschid, L 2016, ‘Big data challenges and opportunities in financial stability monitoring’, Financial Stability Review, vol. 20, pp. 129-142.
Hegazy, S, Sangster, A & Kotb, A 2017, ‘Mapping forensic accounting in the UK’, Journal of International Accounting Auditing and Taxation, vol. 28, pp. 43-56.
Huber, W 2016, ‘Forensic accounting, fraud theory, and the end of the fraud triangle’, Journal of Theoretical Accounting Research, vol. 12, no. 2, pp. 28-48.
Jofre, M 2017, ‘Fighting accounting fraud through forensic analytics’, PhD thesis, The University of Sydney, Australia.
Krahel, JP, & Titera, WR 2015, ‘Consequences of big data and formalization on accounting and auditing standards’, Accounting Horizons, vol. 29, no. 2, pp. 409-422.
Kokina, K & Davenport, TH 2017, ‘The emergence of artificial intelligence: how automation is changing auditing’, Journal of Emerging Technologies in Accounting, vol. 14, no. 1, pp. 115-122.
Massaro, M, Dumay, J & Guthrie, J 2016, ‘On the shoulders of giants: undertaking a structured literature review in accounting’, Accounting, Auditing & Accountability Journal, vol. 29, no. 5, pp. 767-801.
Nurunnabi, MJ 2017, ‘Transformation from an oil-based economy to a knowledge-based economy in Saudi Arabia: the direction of Saudi Vision 2030’, Knowledge Economy, vol. 8, no. 2, pp. 536-564.
Popoola, O, Rayaan, B, Samsudin, R & Ahmad, A 2016b, ‘The moderating role of capability element of fraud on internal industry factors and fraud prevention in Saudi Arabian banking sector’, International Conference on Accounting Studies (ICAS), 15-18 August. Malaysia: University Utara Malaysia.
Popoola, OMJ, Ahmad, AB, Samsudin, RS, Salleh, K & Babatunde, DA 2016b, ‘Accountants’ capability requirements for fraud prevention and detection in Nigeria’, International Journal of Economics and Financial Issues, vol. 6, no. 4, pp. 1-10
Sutton, S, Holt, & Arnold, V 2016, ‘“The reports of my death are greatly exaggerated”— artificial intelligence research in accounting’, International Journal of Accounting Information Systems, vol. 22, pp. 60-73.
Vasarhelyi, M, Kogan, A & Tuttle, B 2015, ‘Big data in accounting: an overview’, Accounting Horizon, vol. 29, no. 2, pp. 381–396.
Warren, DJ, Moffitt, KC, & Byrnes, K 2015, ‘How big data will change accounting’, Accounting Horizons, vol. 29, no. 2, pp. 397-407.
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