Traditional Audit Research Articles

Study on Audit Risk Model Based on Data Mining Algorithm

With the widespread application of modern information technologies such as big data, enterprises have generated a large amount of data in their daily operations. The traditional audit methods have drawbacks such as high cost and high consumption, and can no longer meet the needs of audit work in the digital era. Therefore, it is urgent to adopt audit methods suitable for the digital era to improve audit quality and reduce enterprise Audit risk. For professional auditors in enterprises, it is very important to utilize emerging technologies such as data mining algorithms. The audited enterprise may tamper with its financial statements, and identifying high-quality audit data from massive amounts of data is a huge challenge. Compared to traditional audit methods, data mining algorithms can significantly improve the efficiency and accuracy of audit work. For example, the BP neural network, with its powerful nonlinear mapping ability, can capture complex relationships in data; Support vector machines classify data by finding the optimal hyperplane in high-dimensional space and have good generalization ability; random forest reduces overfitting and improves prediction accuracy by integrating multiple decision trees; Association rule algorithms can discover interesting relationships between data items, helping auditors identify potential fraudulent behavior or abnormal transactions. Therefore, the purpose of this study is to accurately evaluate and reduce the audit risk of enterprises, and to build an audit risk model using computer data mining algorithms. This provides necessary reference and guidance for auditors to conduct data analysis and mine valuable data during the audit process of enterprises, thereby improving audit efficiency.

Evaluation of aerial thermography for measuring the thermal transmittance (U-value) of a building façade

In efforts to mitigate greenhouse gas emissions in the construction sector, policies promoting energy efficiency improvements in existing buildings are being implemented. Accurately assessing the impact of energy retrofit strategies requires precise determination of thermal envelope performance through energy audits. Aerial thermography, employing unmanned aerial vehicles (UAVs) equipped with infrared thermal cameras (IRT), is an underused method overcoming limitations in energy audits of large buildings or complexes. This approach offers an elevated perspective, providing an overview of the building’s thermal patterns, aiding in the identification of thermal envelope defects. By measuring temperature distribution, even in inaccessible envelope areas, the severity of identified defects can be estimated. Beyond detecting thermal bridges, air leaks, and moisture, aerial thermography is valid for estimating thermal transmittance (U-value) of enclosures. The aim of this research is to evaluate this method to calculate the U-value of the façade of an university building located in a Subtropical Mediterranean climate, during a typical full day in different seasons of the year. Concurrently with aerial thermography, the thermometric method (THM) was applied to compare results and refine the operational conditions during U-value measurement using different statistical indices and uncertainty analysis. The results demonstrated the validation of airborne thermography as a method for U-value calculation under specific meteorological and operational conditions, overcoming the common physical limitations of traditional building audit methods.

The Mediating Effect of Information Technology Usage on the Relationship of Internal Audit Processes and Internal Auditor Competence towards Internal Audit Effectiveness: Evidence from the Chinese Financial Sector

The effectiveness of internal auditing is determined by various factors, such as the independence of the internal audit function, auditors’ competence, effective audit planning, efficient audit processes, and support from senior management. The digitalization and informatization of modern enterprises have transformed the way companies are managed and have similarly impacted internal audit practices. Informatization not only alters traditional internal audit processes but also changes auditors’ ways of working and thinking. From the perspective of resource-based theory, the effective allocation and full utilization of human, technological and organizational resources can enhance the effectiveness of internal audit. Therefore, the purpose of this study is to examine the impact of internal audit processes (IAP) and internal auditor competence (IAC) on internal audit effectiveness (IAE), as well as the mediating effect of information technology usage (ITU) on the relationship between IAP and IAC and IAE. To test the proposed hypotheses, a questionnaire survey was conducted within Chinese financial enterprises, yielding a total of 133 valid responses. Statistical data analysis was performed using the partial least squares structural equation modelling (SEM-PLS) method. The results indicate that both IAP and IAC have a significant impact on IAE and ITU. Furthermore, ITU serves as a mediating factor, enhancing the positive contributions of IAP and IAC to IAE.

ARTIFICIAL INTELLIGENCE-BASED AUDIT SOFTWARE: TODAY'S REALITIES AND FUTURE VISION

In the current era of big data, traditional audit methods are proving insufficient, exacerbating the risks businesses face. Artificial intelligence-based audit software (AIAS) is emerging as a promising solution to address these challenges. This study aims to comprehensively examine the current state and future potential of AIAS, analyzing the opportunities and challenges arising from the integration of these technologies into audit processes. To achieve this, we investigate the use cases of AIAS across various audit types and sectors, assessing the benefits they offer and the challenges they present. Additionally, by analyzing the global and local pioneers of AIAS, we identify the factors driving the development of these technologies and uncover future trends. This compilation-based study reveals that AIAS has the potential to make audit processes more efficient, effective, and reliable. However, it also emphasizes the need for careful consideration of issues such as data privacy, algorithmic bias, and ethical implications. This study underscores the importance of collaboration among auditors, businesses, and regulators to fully harness the potential of AIAS while minimizing potential risks. It advocates for investing in a continuous learning and adaptation process. Future research should delve deeper into the impact of AIAS across different sectors and develop recommendations to fully capitalize on the potential of these technologies.

The measurement of fraud perception of investors and the mediating effect of risk aversion: the case of crypto assets

Purpose This study aims to gain a new perspective on auditing by measuring investors’ fraud perception and to reveal the necessity of increasing individuals’ fraud perception by determining the effect of fraud perception on the intention to invest in crypto assets from the investor’s perspective. Design/methodology/approach As part of this quantitative research, a survey was conducted on individuals residing in Türkiye and aged 18 years and above through a convenience sampling method. A total of 446 participants were included in the study. The data collected was analyzed using the partial least squares-variance based structural equation modeling (PLS-SEM) method using the SmartPLS program. Findings Fraud perception causes individuals to be more risk-averse and reduces their intention to invest in crypto assets. At the same time, it has been observed that risk-averse individuals have lower intention to invest in crypto assets. According to the results of the mediating effect analysis, risk aversion behavior partially mediates between the fraud perception and the intention to invest in crypto assets. Among the emotions, only fear increases risk aversion behavior. Among the personality traits, extroversion and openness to experience personality traits reduce risk aversion behavior, whereas neuroticism personality traits increase the intention to invest in crypto assets. Originality/value In an environment where traditional auditing activities are insufficient, increasing investors’ perceptions of fraud can reduce fraud-related losses. In this context, to the best of the authors’ knowledge, the present study might be among the first to investigate the impact of individuals’ perceptions of fraud on their investment intentions in crypto assets.

Using Artificial Intelligence in ESG Assurance

ABSTRACT As environmental, social, and governance (ESG) reporting has become a mainstream channel for companies to communicate their commitment to sustainability issues, the need for reliable and transparent ESG reports is increasing. However, research on ESG assurance is still in its early stages. ESG assurance poses more challenges than traditional financial auditing due to the diverse subjects and types of information in ESG reports. This paper proposes using artificial intelligence (AI) technologies and exogenous data as solutions. It discusses how AI can enhance the efficiency and effectiveness of ESG assurance by assessing vast and extensive data. This paper also explores AI’s application throughout the general ESG assurance process and contributes to the discussion on providing high-quality ESG assurance services. Additionally, it provides practical implications for auditors, regulators, and stakeholders.

Integrated predictive analytics in IT audit planning

This review explores the integration of predictive analytics in IT audit planning, emphasizing its transformative potential in enhancing the efficiency and effectiveness of audit processes. Predictive analytics, which involves the use of machine learning, data mining, and statistical modeling to forecast future events based on historical data, has seen widespread adoption across various industries. In the context of IT audit planning, its application can significantly improve risk assessment, resource allocation, and overall audit execution. The study begins by defining predictive analytics and outlining its key techniques. It then provides an overview of the traditional IT audit planning process, highlighting its critical steps and inherent challenges, such as limited risk visibility and inefficient resource use. By integrating predictive analytics, organizations can address these challenges by leveraging data-driven insights to identify emerging risks, prioritize audit areas, and optimize audit schedules. The integration process involves several stages, including data collection and preparation, model development, and implementation. The review discusses the importance of selecting appropriate data sources, cleaning and preprocessing data, and choosing the right predictive models. It also covers the deployment of these models within existing IT audit frameworks, emphasizing the role of advanced tools and technologies. The benefits of integrated predictive analytics are manifold. Enhanced risk assessment allows auditors to proactively identify and mitigate potential issues, while improved resource allocation ensures that audit efforts are focused on the most critical areas. Additionally, predictive analytics can detect anomalies and patterns that might go unnoticed in traditional audits, thereby increasing audit effectiveness. Case studies of successful implementations in various organizations are presented to illustrate the practical benefits and outcomes of integrating predictive analytics into IT audit planning. The review also addresses potential challenges, such as data quality issues, model accuracy, and organizational resistance, offering strategies to overcome these hurdles. The integration of predictive analytics in IT audit planning represents a significant advancement in audit practices. By adopting these techniques, organizations can enhance their audit capabilities, leading to more proactive and effective risk management. The review provides recommendations for implementation and highlights future trends in predictive analytics and IT auditing. Keywords: Integrated, Predictive Analytics, IT, Aduit Planning.

Modelling business bankruptcy for audit purposes

To facilitate informed decision-making and foster transparency, stakeholders require access to reliable financial information. Financial audits serve the purpose of assisting companies in achieving success by assuring the accuracy and transparency of their financial statements. However, due to the evolving and increasingly competitive nature of markets, companies may exhibit indicators of financial vulnerability, commonly referred to as "red flags." These warning signs could potentially lead to business failure and bankruptcy. To mitigate such risks, predictive models for assessing the likelihood of business failure have been developed. Such models offer valuable decision-making support for auditors, enabling them to identify and mitigate risks associated with financial distress. The primary objective of this study is to develop a predictive model based on logistic regression and compare its effectiveness with traditional audit opinions. The sample comprises Portuguese small and medium-sized enterprises from the textile sector. Data were collected from the SABI database (Iberian Balance Analysis System). For the years 2017 and 2018, 371 insolvent SMEs and 2412 active SMEs were obtained. Through empirical analysis, it was found that regression models possess greater predictive capability compared to conventional audits. The application of these models significantly enhances the accuracy of assessing a company's financial status, thereby enabling professionals to provide more informed and appropriate opinions.

Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning

Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization’s network systems and understand its structure and security procedures, making it difficult to detect this type of behavior through traditional behavioral auditing. This paper proposes a method to leverage unsupervised outlier scores to enhance supervised insider threat detection by integrating the advantages of supervised and unsupervised learning methods and using multiple unsupervised outlier mining algorithms to extract from the underlying data useful representations, thereby enhancing the predictive power of supervised classifiers on the enhanced feature space. This novel approach provides superior performance, and our method provides better predictive power compared to other excellent abnormal detection methods. Using only 20% of the computing budget, our method achieved an accuracy of 86.12%. Compared with other anomaly detection methods, the accuracy increased by up to 12.5% under the same computing budget.

Machine Learning for Anomaly Detection in Accounting Records: A Comprehensive Study

Maintaining the integrity of accounting records is vital for financial transparency, regulatory compliance, and fraud prevention. This study explores the application of Machine Learning (ML) techniques for anomaly detection in accounting records, shedding light on the evolving landscape of financial data analysis. It emphasizes the critical role of accurate accounting records in business operations, financial reporting, and regulatory adherence. Amidst increasing transaction complexity, traditional audit methods face challenges in detecting unnoticed irregularities. A detailed examination of ML methodologies, including clustering, classification, and neural networks, showcases their potential in identifying anomalies within accounting data. The study discusses data preprocessing, feature engineering, model selection, and evaluation criteria essential for robust anomaly detection. Real-world case studies illustrate how ML-driven anomaly detection enhances traditional accounting practices, improving accuracy and efficiency. It underscores ML's proactive role in preventing fraud, errors, and compliance breaches. Ethical and regulatory considerations in ML implementation are addressed, highlighting the importance of transparency, accountability, and responsible AI practices. This study serves as a valuable resource for accounting professionals and regulatory authorities, emphasizing ML's transformative impact on maintaining financial accuracy and regulatory compliance.

Statistics of the Audit Market in China

As the world’s second-largest economy, China has seen an increase in demand for auditing services in recent years. Auditing plays the role of “economic police” and is of great significance to the healthy and orderly development of the market economy. This study does not only enrich the theoretical study of China’s audit market, but also offers suggestions for its improvement, which have practical significance for the sustainable development of China’s audit market. The purpose of this study is to examine the situation on audit market in China, analyze its scale as well as the dynamics of audit in listed companies. As a methodological basis, the author used the works of the Russian scientist E. M. Gutzeit and reports on the audit market of the Ministry of Finance of the Russian Federation. The study reveals that the audit market in China is characterized by intense growth and concentration of large and medium-sized audit organizations; it is still dominated by traditional audit services; audit fees for listed companies are positively correlated with the size of the latter’s assets. The market leaders are the Big Four — Pricewaterhouse Coopers, Deloitte & Touche, Ernst & Young and KPMG. This study gives recommendations to the Ministry of Finance of China and the Chinese Institute of Certified Public Accountants: it is necessary to improve the system of state regulation of the audit market and encourage the establishment of self-regulation mechanisms; to actively expand the scope of audit organizations; to strengthen the branding of domestic audit companies.

Application of Big Data Technology in Audit

In the new era of rapid development of big data technology, the three key points of high-quality development of auditing are auditing talents, auditing rule of law and auditing technology. In the traditional audit process, auditors focus on professional judgment and professional competence, make audit plans, implement audit procedures and draw audit conclusions. However, there are many problems in traditional audit work, such as difficulty in obtaining sufficient external information and inefficiency in analyzing audit evidence. Therefore, auditors apply big data technology to audit work to solve various problems and put forward corresponding safeguard measures.

Determinants of Accounting Students' Career Interest in Becoming Sharia Auditors

The growing demand for skilled professionals in Sharia Financial Institutions (SFIs) has increased the need for individuals experienced in Sharia finance. Islamic principles application in SFIs has made significant changes that can impact the auditing process, necessitating a shift in the traditional audit objectives to align with Islamic principles. Career choice represents a pivotal decision-making process for college students, particularly those in the late stages of academic journey. Correspondingly, this research aimed to investigate the impact of financial rewards, job market considerations, work environment, professional training, Sharia accounting knowledge, and spirituality on accounting students' career interest in becoming Sharia auditors. The research population comprised accounting students from various Indonesian universities, with a sample size of 532 respondents. The purposive sampling technique administering Google Forms was employed for data collection. Subsequently, hypothesis testing was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that financial rewards and job market considerations did not impact accounting students' career interest in becoming Sharia auditors. On the other hand, work environment, professional training, Sharia accounting knowledge, and spirituality significantly positively impacted accounting students' career interest in becoming Sharia auditors. The present research distinguished itself from previous relevant works by providing a more specific analysis of the variables affecting the career preference of accounting students toward Sharia auditing. Additionally, the study expanded the population scope and sample representation by involving accounting students from various universities across Indonesia.

Construction and Discussion of Auditing Curriculum System under the Background of Digital Intelligence

It is an important mission of Chinese universities to train innovative, applied and compound talents who meet the development requirements of the new era. Under the background of the age of digital intelligence, new requirements are put forward for the knowledge structure, innovation ability and comprehensive quality of audit professionals, and the traditional audit curriculum system has been unable to achieve the goal of talent training. It is urgent to restructure the professional curriculum system and improve the quality of auditing personnel training. This paper analyzes the problems existing in the design of the traditional auditing major curriculum system, and based on the digital intelligence technology, deeply discusses and puts forward suggestions to optimize the auditing major curriculum system.

Time Out: Auditor Perceptions and Audit Outcomes When Moving Away from a Time-Based Budget Audit Model

SYNOPSIS The purpose of this study is to examine the benefits and costs of an alternative to the traditional time-based budget audit model. We survey auditors who participated in a real-world, firm-designed, non-time-based budget (NTB) model pilot program during which several engagement teams were not provided with a time budget and auditors did not track time. Results suggest auditors appreciated the autonomy the NTB model allows, felt it improved teamwork, reported less concern about performance evaluations, felt empowered to deliver value-added services to the client, and indicated greater work-life balance and job satisfaction. Firm-provided proprietary archival evidence suggests that efficiency did not differ across the two models, as the timing of audit procedure signoffs was not negatively impacted under the NTB model. Overall, our results suggest a NTB model may be a viable option for audit firms looking to decrease their reliance on time-based budgeting. Data Availability: Please contact Erin M. Hawkins.

The Place of Forensic Accounting in External Audit Practice

This study focused on the place of forensic accounting in external audit practice. The need for auditing was necessitated following separation of ownership from control. Auditing requires the external auditor to carry out an unbiased examination and investigation of the financial statement of any firm with a view to express an opinion on its truth and fairness in accordance. On the other hand, forensic accounting or auditing is a special audit for the purpose of litigation in the court of law. This implied that it required special methodological approach which is completely different from traditional or ordinary audit practice. Hence, this study specifically examined the need, skills, practice of forensic accounting and the relationship between forensic and traditional accountants or auditors. It is a library type of research. It solely employed secondary source of information. Information were obtained from textbooks, Journal articles, seminar papers, thesis etc. Having examined various related literature, it was revealed that the need for forensic accounting was to track crimes in various ways for the purpose of litigation in the court room and it is absolutely different from traditional audit because of exceptional skills required which can enhance external audit practice. It recommended that all professional accountants or practicing accountants should acquire knowledge in forensic accounting skills and all universities should run degree course in forensic accounting in order to aid knowledge of forensic skills in Nigeria.

Stop Auditing and Start to CARE: Paradigm Shift in Assessing and Improving Supplier Sustainability

Traditional auditing has been commonly practiced by multinational companies to monitor their suppliers for sustainability violations. Based on a collaborative supplier sustainability performance improvement program at Koninklijke (Royal) Philips N.V., we introduce a framework that offers a paradigm shift to an improvement-based proactive approach that makes use of suppliers’ self-assessments. We refer to this framework as CARE, consisting of the following phases: collecting supplier sustainability data, assessing suppliers’ sustainability levels, reacting to future violations proactively, and enhancing sustainability performance. The framework integrates analytics techniques to understand the link between the general characteristics of the carefully assessed suppliers—such as location, size, and sector—and their sustainability profile, enabling large-scale supplier assessment and improvement. This information is then used to leverage machine learning techniques to predict current and future sustainability levels of suppliers and to determine best actions for sustainability improvement using mathematical programming. The utilization of analytics constitutes a pivotal element in this endeavor and notably makes CARE highly scalable because it harnesses limited supplier data—namely, only general supplier information—while there is a need to support decision making concerning thousands of suppliers. Philips makes use of this framework and reports that the overall 2021 year-on-year improvement in sustainability performance was 24% for suppliers that entered the program in 2020, indicating the efficacy of the suggested approach. History: This paper was refereed. Funding: The authors gratefully acknowledge the support of TKI Dinalog–Dutch Institute for Advance Logistics on the project entitled “Supplier Sustainability Improvement” [Grant 2017-2-132TKI].

Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) with blockchain-based incentive mechanism

The Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) represents a progressive solution for resolving trust-related issues linked to third-party auditors (TPAs) within traditional healthcare cloud storage audit systems. As the healthcare industry increasingly relies on cloud storage, concerns surrounding security and privacy have grown, motivating the development of a unique incentive mechanism. This mechanism leverages the non-tamperable and traceable features of blockchain technology to encourage TPAs to uphold honesty and reliability. By organizing TPAs as a group of nodes on the blockchain, a system of mutual surveillance is established, enabling diligent monitoring and penalization of any malicious actions. TCSHAS encompasses a comprehensive system model that incorporates smart contracts to manage transaction-related matters, including dispute resolution. Performance evaluations have confirmed its efficiency and suitability for real-world healthcare applications. Our experiments demonstrate that TCSHAS performs well in terms of gas consumption and exhibits scalability as the number of participant’s increases. Compared with other common smart contracts, TCSHAS maintains a balanced level of complexity, incorporates strong security measures, offers comprehensive auditing capabilities, and remains flexible. To further enhance our research, we can explore ways to optimize TCSHAS scalability and performance, such as through shading, as the volume of healthcare data continues to grow. In addition, we can investigate the integration of advanced privacy technologies or AI-based auditing. These advancements will reinforce the role of TCSHAS in establishing trust and security in healthcare cloud storage audits, making it highly relevant in real-world healthcare settings. Ultimately, TCSHAS contributes to improving trust and security in healthcare cloud storage auditing, ensuring responsible management of sensitive healthcare data.

Intelligent auditing techniques for enterprise finance

Abstract With the need of social and economic development, the audit method is also continuously reformed and improved. Traditional audit methods have defects of comprehensively considering various risk factors, and cannot meet the needs of enterprise financial work. To improve the effectiveness of audit work and meet the financial needs of enterprises, a solution for intelligent auditing of enterprise finance is proposed, including intelligent analysis of accounting vouchers and of audit reports. Then, Bi-directional Long Short-Term Memory (BiLSTM) neural network is used to classify the audit problems under three text feature extraction methods. The test results show that the accuracy, recall rate, and F1 value of the COWORDS-IOM algorithm in the aggregate clustering of accounting vouchers are 85.12, 83.28, and 84.85%, respectively, which are better than the self-organizing map algorithm before the improvement. The accuracy rate, recall rate, and F1 value of Word2vec TF-IDF LDA-BiLSTM model for intelligent analysis of audit reports are 87.43, 87.88, and 87.66%, respectively. This shows that the proposed method has good performance in accounting voucher clustering and intelligent analysis of audit reports, which can provide guidance for the development of enterprise financial intelligence software to a certain extent.

An overview of cloud storage data auditing schemes

With the rapid growth of the internet today, cloud storage services have had an impact on numerous internet users worldwide. Internet users, in order to avoid potential risk of data loss, can outsource their local data to remote cloud servers instead of using local media. However, this kind of cloud storage services is sometimes unreliable enough and the data security of users cannot be guaranteed. In this case, data auditing mechanisms for cloud storage are studied to avoid the destruction of user data. The traditional audit schemes are based on provable data possession mechanisms, while the third-party organization relies on the recent blockchain-based methods, conventional single-cloud storage system and recently emerging multi-cloud storage models. Some audit schemes not only provide auditing capability, but also have some other ancillary functions to ensure applicability for different potential malevolent situations. Gathered correlated studies about data auditing schemes of cloud service are analyzed and classified in this paper, based on research objects and research methods in order to seek probable innovation points. We also analyze experiments in their papers. Toward the finish of this review, we sum up all the examination, list the bearings not investigated, and give a few creative spots.