Audit Process Research Articles

Evaluation of the Impact of Artificial Intelligence on the Systems Audit Process

The paper analyzes the impact of artificial intelligence (AI) in systems auditing, fastening on process optimization through the use of advanced technologies similar as intelligent independent systems. A comprehensive literature review was conducted to understand the operation of AI in checkups, revealing that the integration of these technologies has increased inspection delicacy by over to 93. Specific ways similar as cross-validation (CV), support vector machines (SVMs), and artificial neural networks (ANNs) were employed, demonstrating their effectiveness in perfecting delicacy, receptivity, and particularly in anomaly discovery, with results of 87, 90, and 93 independently. The findings emphasize the need to address the ethical and sequestration pitfalls that accompany the use of AI in checkups, given that while these technologies ameliorate effectiveness and delicacy, they also pose significant challenges in terms of ethical and security data running. In this environment, it's recommended that associations invest in training their staff in the use of AI tools, as well as establish clear programs to insure ethics and sequestration. In addition, it emphasizes the significance of continuing to probe and develop new AI operations that will further ameliorate the effects of system checkups in an ever-changing digital terrain. The perpetration of AI not only optimizes processes but also provides a significant competitive advantage by enabling more accurate discovery of irregularities and patterns in large volumes of data. In summary, AI represents a revolution in the field of systems auditing, offering opportunities to improve accuracy and efficiency, although it is crucial to proactively manage the associated ethical and privacy challenges.

Assessment Audit: How Artificial Intelligence Affected Audit Quality of Sustainability Report Based on Auditors Perspective

This study aims to analyze audit assessment and the effects of artificial intelligence on the audit quality sustainability reports from the standpoint of auditors who provide audit opinions on audits that are conducted of sustainable financial reports. Certain audit tasks can now be automated by the audit profession thanks to technology. Artificial intelligence (AI) and big data analytics can be used to analyze huge data, one of the technological innovations that need more study than conventional data. The number of 85 auditors of Accountant Public Firms in Medan completed questionnaires that were used in this study to gather data. Following the collection of data from respondents, quantitative descriptive methodologies were used to analyze the data. The audit profession may improve the quality of audits by promoting the use of AI and big data analytics and by supporting auditors in developing their skills to stay current with new technologies. AI can improve audit quality, reduce costs, and eventually replace human workers. People are still required in the audit process, though, as AI cannot make decisions when it comes to providing audit opinions on financial reports. As a result, its contribution to the audit process is minimal. Therefore, it is hoped that this study will shed light on the potential applications of AI in the auditing process.

Atrial Fibrillation Prevalence In Ischemic Strokes In An Irish Hospital. Spotlight On Anticoagulation Failure In Patients On Direct Oral Anticoagulants

Abstract Background Global atrial fibrillation (AF) burden in acute ischemic stroke patients approximates at 30%. An Audit in 2022, in the concerned hospital identified lack of compliance and under-dosing as major causes of ischemic strokes whilst on direct acting anticoagulants (DOACs). This year, we sought repeat the audit of process with focus on ischemic strokes with pre-existing AF patients established on DOACs, to study trends related to anticoagulation failure. Methods The study was a retrospective observational audit study of all patients admitted with a diagnosis of acute ischemic stroke to the concerned hospital from January 2023 to December 2023 who had known diagnosis of atrial fibrillation and were prescribed a DOAC prior to their admission in the concerned hospital with an ischemic stroke. Patients were identified via HIPE coding and in-hospital stroke registry. Data was collected via charts review, compiled via flow charts, graphs and excel sheets, and studied. Results 143 patients were admitted to the hospital’s stroke service with ischemic stroke in the set time frame. 46 (32%) had Atrial Fibrillation (AF) which was deemed to be the cause of their stroke. 29 (20.2%) had pre-existing diagnosed AF, and 22 of those patients were prescribed DOACs. In all patients already on DOACs, compliance was found questionable or lack of in 11 (50%). 8 (36.3%) patients were on low dose (adjusted dose DOACs). 3 patients were diagnosed with DOAC failure without cause. All new and existing AF patients not anticoagulated, were commenced on anticoagulation. Conclusion The audit showed under dosage and poor compliance were major causes of anticoagulation failure in stroke patients who were anticoagulated with DOACs. Similar results were noted as secondary results in last year’s audit in the concerned hospital. As part of quality improvement process, a specialist DOAC clinic was being set up with further audit process planned for the coming year.

The Influence of Sustainable Risk Management on the Implementation of Risk-Based Internal Auditing

Risk management exerts a significant influence on the competitiveness of an organization and its operational processes. It presents opportunities for expansion, foresight, and the need to promote sustainability. Many organizations have executed comprehensive risk management processes. Moreover, internal audit has increasingly attracted managers’ attention, forming the basis of modern governance methods. The aim of this study is to examine, from the viewpoint of the agency, the impact of the role of internal auditors, training in risk management, and management support on risk management. Following this, the work examines risk management in terms of the risk-based auditing implementation that is performed by Saudi public organizations. This study encompassed 234 completed and therefore valid questionnaires from the manager and assistance of the internal audit department, internal auditors, accountants, and managers employed in Saudi public organizations. The data collected have been analyzed utilizing Smart Partial Least Squares (SmartPLS). This study’s findings confirmed that there is a significant association between the role of internal auditors in risk management, training in risk management, and between management support and risk management. There is also a significant association between risk management and the implementation of risk-based internal auditing. This study’s findings have significant ramifications for those in charge of public-sector organizations, and sustainability, aiming to enhance the dependability and trustworthiness of the internal audit process and other aspects of financial reports and audits in general. Currently, there is a dearth of published research on the factors that influence risk management and also on risk-based internal auditing. This study contributes to the emerging literature on this subject by examining Saudi public organizations; it also establishes empirical variables through a thorough review of relevant research. Conducted here is an empirical investigation that identifies the factors that affect risk management and then its influence on risk-based internal auditing implementation in the economic system of Saudi Arabia. By focusing on Saudi public organizations, this article highlights other countries that have similar systems of governance rules and procedures in their government-operated entities.

Blockchain‐based transparent and certificateless data integrity auditing for cloud storage

SummaryCloud storage provides a convenient way of collaboration and sharing. An increasing number of users are becoming more open to sharing their data with others. Consequently, the integrity of shared data has started to draw significant attention due to the loss control of it. Remote data integrity techniques can solve this problem. To resist the collusion between the third party auditor and the cloud server, existing integrity auditing schemes utilize blockchain to replace the third party auditor to do some work. However, these schemes still involve collusion between the third party auditor and the miners and cannot guarantee the third party auditor to execute the auditing process honestly. Considering this, we propose a blockchain‐based transparent and certificateless data integrity auditing scheme. Specifically, we design the smart contract combining the commitment technology to generate the challenge information and record the auditing information. Besides, we combine certificateless cryptography and the blind technology to achieve the privacy‐preserving. The proposed scheme can resist collusion, improve the transparency of the auditing process, protect the data privacy and reduce the overhead of certificate management and key management. The security analysis and performance evaluation demonstrate the security and efficiency of the scheme.

AUDITORIA DE CONFORMIDADE LEGAL AMBIENTAL: PERSPECTIVAS, DESAFIOS E IMPORTÂNCIA

Environmental legal compliance audits have established themselves as an essential tool in modern environmental management, being increasingly used by companies and organizations that seek to ensure the adequacy of their activities to environmental standards. The audit process involves a systematic assessment of an organization’s processes, products and activities, verifying whether they comply with the legal requirements applicable by current environmental laws, provisions and standards. There are several types of environmental legal compliance auditing. This article seeks to explore in depth the main aspects related to environmental legal compliance audits, addressing their concepts, objectives, application methods, practical challenges and strategic importance in the current context in relation to legal compliance audit processes relevant to the NBR ABNT standard ISO 14.001:2015, Environmental Due Diligence and CONAMA Resolution 306/2002. Despite it is a powerful control tool, environmental legal compliance auditing faces practical challenges, which can compromise its effectiveness if not properly managed. Although the legal assessment of ISO 14001, environmental Due Diligence and the environmental audit of CONAMA Resolution 306 share the common objective of promoting environmental compliance, each has different focuses and is applicable in different contexts. ISO 14001 focuses on continuous and internal management, while environmental Due Diligence is focused on specific transactions and hidden risks. The CONAMA 306 audit offers a broader view of compliance and environmental performance, focusing on large projects and legal inspection. Thus, each approach presents specific advantages and challenges, being complementary in many business situations, especially when it comes to companies that deal with major environmental impacts.

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.

Model for detecting anomalies and suspicious transactions on financial and non-financial entries

Some conventional audit methods sometimes fall short in identifying subtle irregularities and emerging fraudulent schemes. Our proposal is therefore part of the aim of improving audit processes. At the end of the experiments on two sets of data, our model offers the best results compared to other models, particularly in terms of CPU execution time of the model and also in terms of performance in the detection of normal data with a better rate of false positives.

CHALLENGES AND OPPORTUNITIES OF DIGITAL AUDITING: A LOOK BEYOND THE YEAR 2020

Digital technologies, including artificial intelligence (AI), big data, and blockchain, have significantly changed the auditing landscape. This study aims to explore the impact of these technologies on audit practices in the future, focussing on the challenges and opportunities auditors face. Using a qualitative approach, the study involved in-depth interviews, case studies, and participatory observations of several companies adopting digital technology in their audits. The study results show that applying digital technology can increase efficiency, accuracy, and transparency in the audit process. AI and big data enable faster and more comprehensive data analysis, while blockchain improves the reliability and integrity of audited data. However, the study also reveals significant challenges, such as a lack of technology skills among auditors, concerns related to data security and privacy, and potential changes in the role of auditors that could affect audit quality. On the other hand, the digital era also opens opportunities for auditors to engage in multidisciplinary collaboration and develop new competencies relevant to modern technology. However, to make the most of these opportunities, auditors need to maintain strong ethical principles and comply with applicable regulations.

Evaluasi Prosedur Audit atas Persediaan Obat-Obatan pada Rumah Sakit X Oleh Kantor Akuntan Publik ZYX

Inventory of medicines in hospitals is an important and significant asset. The inventory of medicines must be adjusted to meet the substantial treatment needs of the hospital, as inadequate pharmacy efficiency can prevent healthcare services in the hospital. The inventory of medicines in hospitals hold significant importance as it impacts the quality of service delivery. Therefore, auditors perform inventory audit procedures to verify that the inventory reported in the financial statements is consistent and accurate with the actual conditions in the field. The purpose of this research is to evaluate the performance of auditors in auditing financial statements, especially focusing on inventory accounts. The method used is a qualitative descriptive method with primary data sources from interviews with auditors at the Public Accounting Firm (KAP). Advices for auditors at KAP ZYX are improving their skills and knowledge regarding the audit process or the latest regulations through training and experience that can increase their insight.

How does the Chinese digital transformation of enterprises affect the auditor switch?

The digital transformation of enterprises has brought great changes to the audit service demand of enterprises and the audit service supply of auditors. Therefore, there is a pressing need to study the digital transformation of enterprises and its impact on auditor switch. This paper aims to explore the influence of enterprise digital transformation on auditor switch and its potential implications for improving auditor efficiency. Using Python's machine learning tools and text analysis methods, This paper measure the digital transformation of Chinese listed companies and study the impact of digital transformation on the frequency of auditors witches. Our findings suggest that companies that have undergone digital transformation have reduced the frequency of changing auditors by alleviating information asymmetry, enhancing the effectiveness of companies' internal controls and increasing audit fees. This unique measurement mode of digital transformation provides new evidence for the relationship between the audit service demand of enterprises and the audit service supply of auditors under the digital transformation environment. The research can assist businesses in understanding how digital transformation affects auditing professionally and in improving their audit processes accordingly.

Determinants of Internal Audit Effectiveness in Local Government Authorities (LGAs): Evidence from Morogoro Region of Tanzania

This study examines the determinants of internal audit effectiveness in Local Government Authorities (LGAs), addressing the growing importance of robust Internal Audit Function (IAF) in public sector governance. The study employed the descriptive research design based on a mixed research approach integrating both qualitative and quantitative approaches. Data were collected from 45 purposively selected respondents with extensive auditing experience. A multiple linear regression model was used to assess the impact of these factors on internal audit effectiveness. The empirical results indicate that the four factors account for 65.7% of the variance in internal audit effectiveness, with IT utilization being the most significant contributor (R² = 0.657, Adjusted R² = 0.616). The ANOVA results further validate the model, showing a statistically significant F-statistic of 3.2976 with a p-value of 0.001, indicating the robustness of the findings. Top management support was found to be critical in fostering a culture of accountability, while the independence of internal audit ensures objectivity and unbiased assessments. IT utilization was identified as a key driver in enhancing the efficiency and reliability of audit processes. Finally, the competence of internal auditors was shown to significantly influence the quality and accuracy of audit outcomes. The study recommends the need for institutionalizing top management support, safeguarding auditor independence, advancing IT utilization, and promoting continuous professional development for auditors. These measures are essential for improving governance and accountability within LGAs, thereby contributing to more effective and reliable internal audit practices.

The assessment of wemaps audit requirements based on deep learning

ABSTRACT As a specialized map product, Wemaps must comply with relevant laws and regulations. Map audit plays a crucial role in ensuring map quality by preventing the production and dissemination of problem maps, as well as safeguarding national sovereignty, security, and interests. The user base for Wemaps is diverse, encompassing various types of maps, vast amounts of map data, and high expectations for timely dissemination. However, the current map audit process is inefficient and burdensome, failing to meet the specific needs of Wemaps audits. The key to solving this problem lies in the ability to automate and rapidly assess the audit requirements of Wemaps, approving those that require audit and promptly releasing those that do not. This study aims to establish an automated Wemaps audit assessment model using convolutional neural networks and transfer learning methods. By doing so, the burden of map audit can be reduced, and dissemination efficiency can be improved. The main contributions of this study are as follows: (1) Establishment of a dataset for assessing Wemaps audit requirements. (2) Utilization of VGG16 and ResNet50 neural network models for assessing Wemaps audit requirements; (3) Development of an optimal Wemaps audit assessment model through various experiments and training methods. (4) Analysis of factors influencing audit assessments based on measurement indicators and visualized results of the model. The experiments demonstrate that this method achieves high accuracy and can provide assessment services for public map audit requirements.

Towards Better Financial Governance: The Urgency of Model Risk Management in Indonesia Viewed Through Maqashid Shariah

This study assesses the adequacy of Model Risk Management (MRM) frameworks within Indonesian Islamic banking by utilizing Maqashid Shariah principles as a tool to align these frameworks more closely with global standards, specifically focusing on safeguarding life (Hifzh Al-Nafs) and property (Hifzh Al-Mal). Through a comparative literature review, this research examines the structure and effectiveness of MRM practices in Indonesia, referencing regulations such as POJK 65/2016 and SEOJK 25-SEOJK.03-2023, against those in developed financial markets such as the USA, UK, Europe, Japan, and UAE, with an emphasis on governance, lifecycle management, and internal audit processes. The findings suggest that Indonesia’s current MRM frameworks are somewhat rudimentary when contrasted with international norms. Key areas identified for enhancement include expanding the scope, enhancing governance structures, refining model lifecycle management, and strengthening internal audits and oversight mechanisms. By employing Maqashid Shariah principles as an analytical tool, this paper proposes enhancements to MRM frameworks that can significantly improve the stability and compliance of Islamic banks in Indonesia, promoting greater global competitiveness and adherence to Shariah principles. This approach not only integrates traditional MRM approaches with Islamic ethical standards but also provides a tailored framework for regulatory enhancements that support a stable, compliant, and ethical Islamic banking environment.

Fishing event detection and species classification using computer vision and artificial intelligence for electronic monitoring

Fisheries regulations require detailed catch reporting on commercial fishing vessels. Vital components for the sustainable management of fish stocks include a robust estimate of the number of fish caught and the species composition. Catch recording is often done manually by human observers on fishing vessels. Human observers are costly, and consistent data streams can be subject to observer availability and the weather. On-vessel cameras (electronic monitoring, EM) are a growing alternative to human observers. However, on-land human auditors are required to review hundreds of hours of videos recorded during fishing trips that can last for weeks. In this paper, a framework is presented to automatically detect fish in EM videos, count the total fishing events, and classify the fish species. For this purpose, a deep learning and computer vision-based model is developed to efficiently detect fish and fishers onboard a vessel. Secondly, a vision-based tracking pipeline tracks the detected fish and counts the total fishing events in the videos. Thirdly, the extracted fishing events are classified through a deep learning-based fish species classifier, to provide the distribution of different fish species caught for a fishing trip. For our experiments, the datasets were prepared using the electronic monitoring data of multiple fishing trips of a fishing vessel. The videos were recorded on Australian longline vessels targeting tunas and billfish. For the fish detection task, video frames were extracted and labelled manually to provide a digital ground-truth. For the fish species classification task, hundreds of fish images of multiple species were cropped to provide a training dataset for the fish classifier. For the fish counting task, manual counts for the fishing events of individual fish species were generated for the test fishing trips. The developed fish and fisher detector achieves a mean Average Precision of 87.0 % for fish and 94.0 % for fishers on test video frames. The fishing event detection pipeline achieves an Average Precision of 81.0 % and an Average Recall of 74.5 % on test videos. The fish species classifier achieves an Accuracy (Top-1) of 91.11 % for the classification of cropped fish images and 89.05 % for the classification of extracted fishing events from the videos. Experimental results show that our proposed computer vision and artificial intelligence-based solution for video analysis has great potential to automate the auditing process from electronic monitoring footage and contribute to the sustainable management of fish stocks.

PENGARUH PENGUNGKAPAN HAL AUDIT UTAMA, UKURAN PERUSAHAAN, DAN PROFITABILITAS TERHADAP AUDIT REPORT LAG

Auditors will need to note any major problems found during the audit in 2022 when the Audit Standard on disclosing important audit items is implemented. This process of identifying significant issues affects the audit completion time. In addition to key audit matters, company size and profitability also influence the duration of the audit. This study aims to analyze the impact of disclosing key audit matters, company size, and profitability on audit report lag. The method applied is multiple regression analysis on companies listed on the Indonesia Stock Exchange for the period of 2022-2023. The findings indicate that disclosing key audit matters can shorten audit report lag; the more key audit matters disclosed, the faster the audit process can be completed. Company size has a negative impact on audit report lag, while profitability does not affect audit report lag. The study highlights that shortening audit report lag is crucial for reducing economic uncertainty. When auditors can complete the audit report earlier, stakeholders can make better decisions based on accurate and up-to-date information, thus reducing economic uncertainty and the information gap between management and other stakeholders.

BOARD CHARACTERISTICS AND AUDIT QUALITY OF LISTED CONSUMER GOODS FIRMS IN NIGERIA

The study investigates the effects of board characteristics on the audit quality of publicly traded listed consumer goods firms in Nigeria from 2013 to 2022. An ex-post facto research approach was used in this study. For data analysis, the panel regression technique was used in the study. The study's findings revealed that board independence and board financial expertise have a positive and significant effect on the audit quality of Nigerian listed consumer goods companies. However, board gender diversity had a negative and insignificant effect on the audit quality of Nigerian-listed consumer goods companies. The study concludes that boards with more independent members who are not influenced by management may improve the audit process efficacy. To improve audit quality, listed consumer goods firms should focus on retaining board independence and guaranteeing the presence of directors with significant financial experience. While the relationship between board gender diversity and audit quality is still uncertain, it is critical to promote diversity and inclusivity on corporate boards in order to build a more robust governance structure. Continuous monitoring and research in this area will contribute to a better understanding of the relationship between gender diversity and audit quality.

Research on the Application of AI Technology in Auditing

With the rapid development of AI technology, the application in the field of auditing has become an important means to improve the efficiency and quality of auditing. AI technology has significantly improved the efficiency and quality of audit work by automating data analysis, risk assessment, and audit processes. However, as technology evolves, so do challenges such as data privacy, security, algorithmic transparency, and lack of talent. In order to address these issues, it is recommended to strengthen data protection, improve the accuracy and transparency of algorithms, formulate unified AI audit standards, and focus on cultivating audit talents with AI skills. In the future, the audit industry needs to actively manage and control risks while enjoying the convenience brought by AI, ensure the quality and credibility of audit work, and promote the development of audit services in a more efficient and intelligent direction.

Assessing the Integration of ChatGPT in IT Audits that Support Financial Statement Audits

Artificial intelligence (AI) has brought significant changes in many fields, including the audit sector. Generative AI, a type of AI, leverages deep learning models to generate human-like content like images and words. ChatGPT, a type of generative AI, is a language model capable of revolutionizing information technology (IT) audits in support of financial statement (FS) audits. This paper highlights and examines advantages and disadvantages of incorporating ChatGPT into IT audits that support FS audits. ChatGPT offers benefits such as efficiency, precision, and speed, which are vital in the IT audit process. However, it also comes with challenges that need to be addressed. This paper contributes to the growing body of knowledge related to the use of ChatGPT in IT audits that support FS audits. The paper further provides insights into how audit firms can effectively adopt ChatGPT to improve the quality, efficiency, and effectiveness of their IT and FS audits.

Research on Optimization of Internal Audit Process under the Background of Big Data

In the era of big data, it is especially vital to optimize the internal audit process. Traditional methods are increasingly inadequate to handle the burgeoning volume of data and intricate environments. Consequently, leveraging big data technology to enhance audit processes has become essential. The role of internal audit should evolve from mere supervision to a service-oriented function, aiming to bolster governance, uncover issues, and enhance value. Nevertheless, challenges such as inadequate audit independence and feeble rectification mechanisms persist. The integration of big data technology with internal audit practices can markedly elevate the quality and efficiency of audits, and foster innovation in audit methodologies. To optimize the audit process, it is imperative to define clear objectives, propose practical and viable solutions, and further refine the quality and sophistication of audit work through techniques like data preprocessing and analysis.