Intelligent Process Automation Research Articles

Advances in auditing and business continuity: A study in financial companies

This paper delves into the integration of Intelligent Process Automation within the domain of business continuity auditing, with a focus on the Portuguese banking sector. In an era marked by rapid technological advancement, organizations are increasingly leveraging automation to reinforce operational efficiency and realize substantial cost savings. Concurrently, auditors play a pivotal role in ensuring seamless transitions amid technological transformations to safeguard business continuity. This research endeavors to bridge the realms of business continuity and intelligent automation, culminating in a comprehensive application that streamlines the audit process. The implemented solution encompasses the automation of critical audit activities, including communication, information requests, and final report submissions, liberating auditors from the chains of repetitive tasks. The incorporation of business intelligence augments this automation framework, enabling a meticulous analysis of key performance indicators within the audit department. This ensures a continuous evaluation of the efficacy of the Annual Audit Plan. Empirical validation of this initiative was achieved through surveys conducted with audit teams from four prominent Portuguese banks. The results unequivocally affirm the potential benefits of this implementation, extending invaluable support to management in the decision-making process, while concurrently alleviating auditors of routine tasks inherent to the audit process. This study not only underscores the transformative potential of intelligent process automation in the audit domain but also offers a replicable framework for organizations seeking to fortify their business continuity efforts through technological integration. The findings hold implications for businesses navigating the dynamic intersection of technology and audit practices, providing a blueprint for harnessing automation for enhanced operational resilience.

Handwriting identification and verification using artificial intelligence-assisted textural features

Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

Towards Intelligent Automation (IA): Literature Review on the Evolution of Robotic Process Automation (RPA), its Challenges, and Future Trends

Abstract Robotic Process Automation (RPA) and Artificial Intelligence (AI) integration offer great potential for the future of corporate automation and increased productivity. RPA rapidly evolves into Intelligent Process Automation (IPA) by incorporating advanced technologies and capabilities beyond simple task automation. The paper aims to identify the organisational, technological, and human-centred challenges that companies face in transitioning from RPA to IPA. The research process involved conducting the scientific literature search using the ResearchRabbit AI tool, which provided a set of reference papers relevant to the formulated research questions. As a result of the conducted literature review, the authors identified key challenges and possible countermeasures for companies transitioning from RPA to IPA. The resulting collection of reference scientific articles formed the basis for this study’s content and substantive analysis. Furthermore, this study contributes by identifying artificial intelligence techniques and algorithms, such as Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), predictive analytics, and others, that can be integrated with RPA to facilitate the transition to IPA. The paper also offers insights into potential future research areas.

Integrating Data Engineering with Intelligent Process Automation for Business Efficiency

Integrating Data Engineering with Intelligent Process Automation for Business Efficiency

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City.

The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process.

Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions

The aim of this paper is to present a systematic literature review of the existing research, published between 2006 and 2023, in the field of artificial intelligence for management information systems. Of the 3946 studies that were considered by the authors, 60 primary studies were selected for analysis. The analysis shows that most research is focused on the application of AI for intelligent process automation, with an increasing number of studies focusing on predictive analytics and natural language processing. With respect to the platforms used by AI researchers, the study finds that cloud-based solutions are preferred over on-premises ones. A new research trend of deploying AI applications at the edge of industrial networks and utilizing federated learning is also identified. The need to focus research efforts on developing guidelines and frameworks in terms of ethics, data privacy, and security for AI adoption in MIS is highlighted. Developing a unified digital business strategy and overcoming barriers to user–AI engagement are some of the identified challenges to obtaining business value from AI integration.

Implications for Sustainability Accounting and Reporting in the Context of the Automation-Driven Evolution of ERP Systems

This paper delves into the impact of the automation-driven evolution of enterprise resource planning systems (ERPSs) on sustainability accounting and reporting and the associated challenges. By employing a holistic approach focusing on the current characteristics of both ERPSs and automation platforms and an inductive approach to perform a content analysis, this study highlights how the new generation of ERPSs can support the improvement of accounting in general and sustainability accounting and reporting quality in particular. The analysis was based on information provided by the developers of leading ERPSs and automation platforms with a significant worldwide market share. In this paper, we deepen the understanding of the role that ERPSs play in improving sustainability accounting and reporting, focusing on both the benefits and challenges derived from the impact generated by increasing the integration of robotic process automation and intelligent process automation solutions. The results obtained have academic and managerial implications, addressing a research gap concerning the understanding of the consequences of ERPSs evolution from the perspective of professionals and the competitive edge companies can take advantage of to improve sustainability reporting and accounting processes.

Intelligent Process Automation and Business Continuity: Areas for Future Research

Robotic process automation and intelligent process automation have gained a foothold in the automation of business processes, using blocks of software (bots). These agents interact with systems through interfaces, replacing human intervention with the aim of improving efficiency, reducing costs and mitigating risks by ensuring and enforcing compliance measures. However, there are aspects of the incorporation of this new reality within the business continuity lifecycle that are still unclear, and which need to be evaluated. This study provides a multivocal literature review of robotic process automation and intelligent process automation correlated with business continuity, to identify the level of awareness of these two emerging forms of automation within the business continuity management lifecycle. Based on the reviewed literature, the study develops a discussion of the main research areas for investigation, identifying what is attracting the attention of practitioners and researchers and which areas they highlight as promising for future research. Numerous sources from relevant backgrounds reveal an interest in these interrelated topics but there as yet is little or no information available on the direct connection between them.

Intelligent Process Automation: Streamlining Operations and Enhancing Efficiency in Management

Intelligent Process Automation: Streamlining Operations and Enhancing Efficiency in Management

Understanding How Intelligent Process Automation Impacts Business Continuity: Mapping IEEE/2755:2020 and ISO/22301:2019

Understanding How Intelligent Process Automation Impacts Business Continuity: Mapping IEEE/2755:2020 and ISO/22301:2019

Intelligent digital services in russian railways: increasing productivity and automated support for it service availability and continuity

The article discusses the process and state of implementing digital services (software robots and chatbots) in JSC "Russian Railways" in the context of the ongoing global digitalisation of the economy and approaching intelligent automation of business processes. Based on the results obtained, the impact of advanced digital technologies on reducing the labour costs of Russian Railways employees for routine tasks, increasing their labour productivity, improving the quality of services provided to customers and guaranteeing the IT infrastructure's reliability is assessed. The goal of this work is not only to review this emerging issue and assess the degree of company's digital transformation and its impact on the low labour productivity concern, but also to vision the prospects of achieving the major goal to implement the Artificial Intelligence system, with software robots and chatbots as tools, and subsequently the paradigm of intelligent process automation as global trends.

An Information Ethics Framework Based on ICT Platforms

With continuing developments in artificial intelligence (AI) and robot technology, ethical issues related to digital humans, AI avatars, intelligent process automation, robots, cyborgs, and autonomous vehicles are emerging, and the need for cultural and social sustainability through AI ethics is increasing. Moreover, as the use of video conferencing and metaverse platforms has increased due to COVID-19, ethics concepts and boundaries related to information and communications technology, cyber etiquette, AI ethics, and robot ethics have become more ambiguous. Because the definitions of ethics domains may be confusing due to the various types of computing platforms available, this paper attempts to classify these ethics domains according to three main platforms: computing devices, intermediary platforms, and physical computing devices. This classification provides a conceptual ethics framework that encompasses computer ethics, information ethics, cyber ethics, robot ethics, and AI ethics. Several examples are provided to clarify the boundaries between the various ethics and platforms. The results of this study can be the educational basis for the sustainability of society on ethical issues according to the development of technology.

Intelligent Process Automation: An Application in Manufacturing Industry

Background: The intelligent processes automation has been cataloged as one of the most potential and strategic technology solutions to develop a corporate digital transformation. Method: This paper introduces essential concepts to create Intelligent Process Automation (IPA) in industries and proposes a framework to implement IPA technologies successfully. The approach involves: firstly, assembling a good implementation setup and deeply researching the process using process mining techniques. Secondly, choosing and locating the best AI technology inside the IPA. Finally, defining an appropriate architecture of the IPA. Results: The paper illustrates an IPA use case in the manufacturing industry, where it is possible to automate the process of sending production orders to a manufacturing plant and optimize waste and plant capacity significantly. Conclusions: The research depicts the potential of intelligent process automation and its quantifiable benefits in the manufacturing process, and the contribution can be applied to different enterprises with a global context.

Promoting Sustainability through Next-Generation Biologics Drug Development

The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.

Application of Information Digitalization Technology in Audit Process Through Intelligent Process Automation (IPA) Approach

Intelligent Process Automation (IPA) is a technology capable of organizing automation processes for structured, semi-structured, and unstructured data. In audit assignments, there are three types of assignments, namely structured, semi-structured and unstructured assignments. This study aims to identify the possibility of applying IPA (Intelligent Process Automation) in audit assignments. By using IPA technology, it is expected to be able to create efficiency and effectiveness in the audit process. As we know that audit assignments require time and high costs, so IPA technology is expected to be able to shorten audit time and costs without reducing the quality of services provided by auditors. This research is qualitative research using primary data. The respondents in this study are public accountants who work in the Public Accounting Firm (KAP). The research instrument consists of mapping structured, semi-structured, unstructured audit assignments; then identified each with the possibility of applying IPA (Intelligent Process Automation) in each audit process. Respondents in this study were forty-one. The results of the analysis using descriptive analysis and frequency analysis, and it was found that the use of automation of audit procedures at the audit planning stage was still rarely used. With the following details (1) structured assignment, still rarely used; (2) semi-structured assignment, still rarely used; (3) unstructured assignments, sometimes used. Meanwhile, the use of automation of audit procedures at the control and substantive testing stage as well as the overall conclusion (audit completion) stage is sometimes used, both structured, semi-structured, and unstructured assignments.

Intelligent Process Automation of Industries Using Artificial Intelligence and Machine Learning

The application of Disruptive Technologies (DT), using Artificial Intelligence (AI) and Machine Learning (ML), is still a challenge for many industries in the modern age. Quick transformation of business’ models and enhancement of consumer expectation are fundamental elements of Intelligent Process Automation (IPA) to boosting delivery and production of goods and services. In this research contribution, we will evaluate IPAs and their influence in the management of industries. In that case, major elements of AI and ML will be discussed comprehensively. Areas of application and analysis will be discussed in relation to digital industries. The results in this contribution will be used as a recommend action plan for industries to enhance their management and optimization when it comes to AI and ML.

Development of Intelligent Robotic Process Automation: A Utility Case Study in Brazil

Robotic Process Automation (RPA) refers to process automation applications of traditional Information Technologies based on robot software with the ability to capture and interpret the specific processes of organizations. Studies show that RPAs are able to reduce resources and optimize processes effectively in relation to customers. Some of these call center business processes deal with customers most likely to complain; therefore, a “Proactive Notification” robot was developed to classify these types of customers to be prioritized. This robot defines the creation of an RPA architecture for proactive notifications applied to an electric company in Brazil. The methodology used for the development of this project consisted of data management, predictive models, and peripheral components for sending SMS and making calls. It was tested against all customers in 40 cities (two states) and the model considers the historical basis of 3 years of occurrences to predict customers with a high probability of filing a complaint due to power failure. The results show that customers who were called for this type of problem did not call the call center again to complain, suggesting positive acceptance of the robot. In conclusion, the robot presented herein is capable of making proactive notifications with high precision to customers with the highest probability of complaints, predicting possible problems.

Cognitive automation: A new era of knowledge work?

There have been a number of breakthroughs in artificial intelligence since the beginning of the 21st century with machines now outperforming humans in cognitive tasks such as object detection, face recognition, language translation, and complex decision strategies. Computational advances enable machines to process and analyse information at a scale far beyond human capabilities and has led to a rise in demand for intelligent process automation (IPA) services. This article considers the potential for cognitive algorithms to disrupt knowledge work in the modern workplace. Benefits include augmenting and accelerating the intelligence-decision-action cycle that is central to knowledge work. However, there are also risks from becoming over-reliant on algorithms in ambiguous and uncertain real-world situations. The value from next-generation knowledge systems will come from bridging human and artificial intelligence for insights and innovation.

Military Field Camp Installation Improvement Solutions: Lithuania Case

The installation plan, operation and functions of the military field camp are very important for a military unit action. Every army in a NATO country strives to set up a military camp that is as flexible and comfortable as possible. NATO has set standards for the installation of a military field camp, and each country in the organization relies on this standard. This study was focused on how to build a military field camp as quickly as possible and to minimize the loss of various resources. The research developed a focus group survey analysis method to identify weaknesses in the military field camp setting process, accomplished an in-depth interview analysis method with four Lithuanian Grand Duke Vytenis General Support Logistics Battalion Field Camp Installation Experts. Moreover, was accomplished a comparative analysis to compare military field camps plans before and after installation. In addition, an operational process analysis approach was used, which depicts the installation of a military field camp from planning to unit deployment. There was used the Bizagi Modeler v3.7 software to construct the intelligent process automation diagram which showed the military camp installation improvement solutions [18]. Moreover, the in-deep cause analysis method was used, which helped to identify the main problems of the military field camp installation.

A Supervised Machine Learning Approach for Intelligent Process Automation in Container Logistics

Abstract Process control in manufacturing industries usually lacks flexibility and adaptability. The process planning is traditionally pursued within the production scheduling and then remains unchanged until a major overhaul is necessary. Consequently, no process knowledge acquired by the machine operators is used to adapt, and thus improve, the process control. In this paper, a fully automated process control solution for container logistics is proposed, which is based on deep neural networks and has been trained from process steering decisions made by employees. Further, a fully automated framework for the labeling of container images is introduced, making use of inherent properties of the logistic process. This allows to automatically generate data sets without the need for manual labeling by an operator.