scholarly journals CONCEPTUAL FRAMEWORK FOR UNDERSTANDING EMERGING TECHNOLOGIES THAT SHAPE THE ACCOUNTING AND ASSURANCE PROFESSION OF THE FUTURE

2020 ◽  
Author(s):  
Atanasko Atanasovski ◽  
Zorica Bozinovska Lazarevska ◽  
Marina Trpeska
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Conceptual Framework ◽  
Information Needs ◽  
Business Processes ◽  
Industrial Revolution ◽  
Emerging Technologies ◽  
Current Accounting ◽  
Assurance Services

We are currently on the verge of the fourth industrial revolution (industry 4.0) that will fundamentally change the way in which businesses and individuals operate. It is characterized by the integration of both physical and virtual world with a new pulse for greater automation and autonomy of business processes without or limited need of human intervention. Cloud computing, the internet of things, big data and data analytics, artificial intelligence and machine learning, blockchain, augmented reality are among the most exploited contributing technologies. This paper provides a conceptual framework for emerging technologies with greatest disruptive potential for the accounting and assurance profession. We have surveyed the scarce literature with the aim of investigating the relationship and interplay between most potent emerging technologies and accounting and assurance services. Our findings suggest that cloud accounting, big data, blockchain, artificial intelligence and machine learning will most likely shape the accounting and assurance world for many organisations and future skills set pursued by professionals in order to add value. We conclude that current accounting research efforts are insufficient to understand and grasp the possible future interactions between the vibrant digital economy, its emerging technologies and the accounting profession, including the changing information needs of investors.

scholarly journals The Digital Transformation and Disruption in Business Models of the Banks under the Impact of FinTech and BigTech

2020 ◽  
Vol 14 (1) ◽  
pp. 294-305
Author(s):  
Virginia Mărăcine ◽  
Oona Voican ◽  
Emil Scarlat
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Models ◽  
Business Processes ◽  
Digital Transformation ◽  
Operating Costs ◽  
Continuous Increase ◽  
The Impact

AbstractThe explosive development of artificial intelligence, machine learning and big data methods in the last 10 years has been felt in the financial-banking field which has subjected to profound changes aimed at determining an unprecedented increase in the efficiency and profitability of the businesses they carry out. The tendencies of applying the concepts coming from AI, together with the continuous increase of the volume, complexity and variety of the data that the banks collect, store and process have acquired the generic names of FinTech, respectively BigTech. Five main areas exist where Fintechs and Bigtechs can provide improvements in business models for the banks: introducing specialized platforms, covering neglected customer segments, improving customer selection, reduction of the operating costs of the banks, and optimization of the business processes of the banks. We will present some of these improvements, and then we will show how the business models of the banks dramatically transform under the influence of these changes.

scholarly journals Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0

2021 ◽  
Author(s):  
Chen Chuqiao ◽  
S.B. Goyal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Security And Privacy ◽  
Fourth Industrial Revolution ◽  
Data Life Cycle ◽  
Suitable Framework ◽  
Distributed Cloud

The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence. These new digital technologies integrate into the data middle platform have facilitated the progress of industry, promoted the fourth industrial revolution. And it also has caused challenges in security and privacy-preventing. The privacy data breach can happen in any phase of the Big-Data life cycle, and the Data Middle Platform also faces similar situations. How to make the privacy avoid leakage is exigency. The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain. In this research, the researcher reviews the security and privacy-preventing in Big-Data, Machine Learning, Blockchain, and other related works at first. And then finding some gaps between the theory and the actual work. Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform. No only academicians, but also industry practitioners especially SMEs will get the benefit from this research.

IR 4.0. IN PARLIAMENT: CONCEPTUALISING THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE PARLIAMENT OF MALAYSIA’S PARLIAMENTARY QUESTIONS

2020 ◽  
Vol 5 (20) ◽  
pp. 124-137
Author(s):  
Muthanna Saari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Emerging Technologies ◽  
Participant Observation ◽  
Research Data ◽  
Human Values ◽  
Structured Interviews ◽  
Fourth Industrial Revolution ◽  
The Government

The Fourth Industrial Revolution (IR 4.0.) offers significant opportunities to humankind in revitalising human values through which the emerging technologies inevitably seam into daily societal life. Legislatures face ever-increasing challenges in fulfilling their duties in such a complicated society which subsequently entails complex legislations. Parliamentary questions (PQs) as one of the traditional tools utilised by parliamentarians provide a quintessential mechanism to achieve the oversight functions of parliament. However, there are still immense undiscovered potentials of PQs, yet many previous studies have not looked into the content of the questions and the consequences of the response to the conduct of governments. This paper set out to examine the usefulness of IR 4.0. namely, artificial intelligence (AI) and machine learning towards improving the efficiency, transparency, and accountability of parliament and the government. The research data of this exploratory and interpretative study is drawn from three main sources: literature studies, semi-structured interviews, and participant observation of the existing PQs processing in the Dewan Rakyat, Parliament of Malaysia. This study has found that generally, the approval of such technologies introduction to the parliamentary businesses is contingent upon its ability to capture complex considerations in the existing environment.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

2021 ◽  
Author(s):  
Richard Büssow ◽  
Bruno Hain ◽  
Ismael Al Nuaimi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Hybrid Model ◽  
Domain Knowledge ◽  
Spare Parts ◽  
Good Prediction ◽  
First Principle ◽  
Plant Data ◽  
Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

scholarly journals Merging Artificial Intelligence and Blockchain Technologies to Solve Academic Qualification Forgery Issues

2020 ◽  
Author(s):  
Manju Jose
Keyword(s):  
Artificial Intelligence ◽  
Industrial Revolution ◽  
Emerging Technologies ◽  
Integrated System ◽  
Future Research ◽  
Group Discussions ◽  
Academic Qualification ◽  
Negative Impacts ◽  
Economy And Education ◽  
Academic Qualifications

This paper emphasizes the possibility of merging artificial intelligence and Blockchain technologies to solve academic qualifications forgery issues in the educational sectors. Empirical data is collected through interviews with specialists and technical people who are interested in the emerging technologies of the Fourth Industrial Revolution and focus group discussions in the field, as well as from reports in the reviewed literary articles. Scientific journals have also been accessed to analyse the paper goals and objectives. The findings suggest that emerging technologies can be integrated to become more efficient and effective in detecting fraud and forgery before it occurs. Considerable attention should be given to reducing and combating these issues because they have significant negative impacts on the economy and education. Accordingly, the study makes recommendations based on the results and areas of future research, considering the establishment of a unified and integrated system. Initially it will be applied as a pilot in Sultanate of Oman, then gradually it will be extended to the Gulf Cooperation Council States (GCC) and internationally particularly the affiliated and the recognized educational institutions to avoid the phenomena that affects the reputation and quality of education institutions and academic qualifications. The conclusion considers the impacts of the proposed system in the education and economy as well in general.

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