Artificial intelligence and robotic process automation in auditing and accounting: a systematic literature review

Purpose: The growing sophistication of financial fraud in the banking sector has necessitated the adoption of advanced technical solutions such as artificial intelligence (AI) and robotic process automation (RPA) to enhance fraud detection and prevention. This study examines the role, effectiveness, and challenges of AI and RPA in combating financial fraud, addressing gaps left by traditional rule-based systems. Methodology: This study employs a literature review methodology, synthesizing existing research, case studies, and industry reports to evaluate the impact of AI and RPA on fraud detection. Key themes analyzed include real-time analytics, anomaly detection, predictive modeling, operational efficiency, and implementation challenges. Findings: The findings reveal that AI significantly improves fraud detection accuracy, reduces false positives, and adapts to emerging threats, while RPA enhances compliance and operational efficiency by automating repetitive tasks. However, challenges such as algorithmic bias, adversarial AI attacks, data privacy concerns, high implementation costs, and ethical dilemmas around transparency and accountability hinder widespread adoption. Despite these obstacles, financial institutions report substantial reductions in fraud-related losses after integrating AI and RPA. Unique contribution to theory, practice and policy (recommendations): This study contributes to theory by consolidating insights on AI and RPA’s transformative potential in fraud detection. For practice, it recommends investing in explainable AI, robust adversarial defense mechanisms, and cost-effective RPA integration. Policymakers should establish ethical AI governance frameworks, promote regulatory alignment, and incentivize innovation to ensure financial security and transparency. The study underscores that maximizing the benefits of AI and RPA requires continuous technological advancement, ethical oversight, and collaborative regulatory efforts.

The paper’s primary focus is intelligence creation by AI and fast implementation by RPARobotic Process Automation (RPA). It starts with the applicability of the Turing testTuring test propounded by Alan Turing, the father of Artificial Intelligence (AI). At the backdrop lies various events, namely the recent motivation addition of various bank account holders. These factors fuelled the demand for AI and RPAArtificial Intelligence and Robotic Process Automation (AI and RPA) implementation in the banking industry. It pitched how AI and RPA work in real-time scenarios such as financial fraud and money laundering. It discusses how AI builds the knowledge graph and recommends products and services for each customer. This knowledge is implemented and delivered using RPA. The AI application gained prominence in every banking business segment, such as equity, personal, investment and loan. The application of RPA is present in all business segments, although the percentage is increasing yearly. The AI and RPA can help banks to convert the challenges to opportunities. There have been various challenges, and the application of AI and RPA combinations is the key to solving the inefficiencies. Advanced analytical techniques on open-source data have been used in this paper.

This study is set out with the general objective of establishing the effect of digital technologies on the effectiveness of internal audit function in commercial banks in Kenya. The research is guided by the following specific objectives; to determine the effect of artificial intelligence on the effectiveness of internal audit function in commercial banks in Kenya; to determine the effect of cognitive technology on the effectiveness of internal audit function in commercial banks in Kenya; to determine the effect of data analytics on the effectiveness of internal audit function in commercial banks in Kenya; to assess the effect of robotic process automation on the effectiveness of internal audit function in commercial banks in Kenya; to determine the joint effect of digital technologies (artificial intelligence, cognitive technology, data analytics and robotic process automation) on the effectiveness of internal audit function in commercial banks in Kenya. The research employed descriptive research design. The target population for the study was all the 39 licensed commercial banks in Kenya. A census of the population was used in this study. Primary data was collected using closed questionnaires. Descriptive and inference statistics were used to analyze the data with the help of Statistical Package for Social Sciences (SPSS) Version 23 programme. Pearson correlation was used to establish the strength of the independent variable on the dependent variable. Regression (Simple and multiple) analysis was used to establish the effect of the independent variable on dependent variable. The data analyzed was presented in the form of tables, pie charts and graphs. The study examined the effect of digital technologies artificial intelligence (AI), cognitive technology, data analytics, and robotic process automation (RPA) on the effectiveness of the internal audit function in commercial banks in Kenya. The findings indicated that AI and RPA had a positive and significant impact on internal audit effectiveness, whereas cognitive technology and data analytics did not have a statistically significant effect. However, when analyzed jointly, all digital technologies had a significant and positive joint effect on internal audit effectiveness. Develop comprehensive digital transformation strategies: Banks should take a holistic approach in implementing digital technologies to maximize their combined impact on the internal audit function. Regulators should develop policies that guide the ethical and effective use of AI and RPA in internal audit functions. The government and regulatory bodies should incentivize banks to integrate digital technologies to improve transparency and efficiency in auditing.

This research paper investigates the integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) into Enterprise Resource Planning (ERP) systems, examining their role in enhancing business operations and decision-making processes. The study seeks to answer the research question: How do AI and RPA technologies affect the efficiency, effectiveness, and strategic decisions of businesses leveraging ERP systems? The research combines qualitative and quantitative methods, utilizing interviews with industry experts, IT managers, and case studies of enterprises that have adopted AI and RPA technologies within their ERP frameworks. Key findings indicate that AI and RPA significantly streamline business operations by automating routine tasks, enhancing data analysis, and improving decision-making. Companies reported up to a 40% reduction in manual processing time, with substantial cost savings of up to 25%. Additionally, AI's predictive capabilities were shown to improve demand forecasting, inventory management, and supply chain optimization. Despite these benefits, the research also highlights challenges such as integration complexities, data privacy concerns, and employee resistance to automation. Notably, businesses that implemented AI and RPA successfully saw improvements in employee productivity, as routine tasks were automated, enabling workers to focus on higher-value tasks. This research contributes to the growing body of knowledge on digital transformation in business operations and emphasizes the importance of careful planning, risk management, and employee training to ensure the successful adoption of these transformative technologies. The study underscores the transformative potential of AI and RPA in optimizing ERP systems and driving business innovation.

The integration of Artificial Intelligence (AI) and Business Process Automation (BPA) has revolutionized sales efficiency and Customer Relationship Management (CRM) performance across industries. AI-driven automation enables organizations to optimize sales operations, enhance customer interactions, and streamline workflows, resulting in improved productivity and revenue growth. This study examines the impact of AI and BPA on sales efficiency and CRM performance, highlighting the role of predictive analytics, chatbots, natural language processing (NLP), and robotic process automation (RPA) in driving business success. AI-powered predictive analytics enhances sales forecasting accuracy by analyzing historical data, customer behavior, and market trends to provide actionable insights. Automation tools enable sales teams to focus on high-value activities by reducing manual tasks such as lead qualification, data entry, and follow-ups. Chatbots and AI-driven virtual assistants enhance customer engagement by offering instant responses, personalized recommendations, and seamless support. Additionally, NLP-driven sentiment analysis helps businesses understand customer emotions, allowing for proactive problem resolution and improved service quality. The adoption of AI and BPA in CRM enhances customer retention by delivering personalized experiences through automated marketing campaigns, intelligent recommendations, and dynamic content generation. Machine learning (ML) algorithms enable CRM systems to continuously learn from interactions, improving response accuracy and customer satisfaction. AI-powered automation also facilitates seamless data integration across multiple touchpoints, ensuring real-time insights for decision-making. Despite the benefits, challenges such as data security, implementation costs, and workforce adaptation must be addressed. Organizations must ensure ethical AI deployment, data privacy compliance, and employee training to maximize the potential of automation while mitigating risks. Future research should explore the role of generative AI, advanced machine learning models, and AI-driven personalization in further enhancing sales efficiency and CRM performance. This study concludes that AI and BPA significantly improve sales processes, operational efficiency, and customer relationships by enabling intelligent automation, data-driven decision-making, and personalized customer experiences. Businesses that strategically integrate AI-powered automation into their CRM systems will gain a competitive advantage in an increasingly digital marketplace.

Intelligent automation has become a key part of using artificial intelligence (AI) in managing businesses, changing the way companies operate. By combining machine learning, robotic process automation (RPA), and advanced analytics, intelligent automation helps automate both simple tasks and more complex processes. This improves efficiency, encourages innovation, and adds flexibility, helping businesses stay competitive in fast-changing markets. For example, companies in fields like manufacturing, healthcare, and retail have seen major improvements by using intelligent automation in their operations, showing how adaptable this technology is. AI also plays a key role in decision-making. By automating the processing and analysis of large datasets, AI provides managers with reliable information to solve problems and find new opportunities. It helps analyze market trends, improve supply chains, and evaluate risks, making sure decisions are fast, data-driven, and match the company’s goals. AI tools also help businesses predict the outcomes of different strategies, reducing risks and improving decision-making. AI-driven data analysis helps companies understand consumer behavior, predict demand, and analyze finances more accurately, leading to better decisions and long-term growth. While AI brings many benefits, the research also looks at challenges companies face. These include concerns about data security, ethical issues, high costs of implementation, and the need for skilled workers to manage and optimize AI systems. Solving these challenges is essential to make sure AI adoption is effective, ethical, and long-lasting. To fully take advantage of AI, businesses should focus on being transparent, invest in training their employees, and encourage teamwork to reduce risks and get the most out of AI

In today’s dynamic business environment, organizations face growing demands to enhance operational efficiency, reduce costs, and make informed decisions. To remain competitive, businesses are increasingly turning to Intelligent Automation—a strategic convergence of Robotic Process Automation (RPA) and Artificial Intelligence (AI). While RPA streamlines repetitive, rule-based tasks, AI adds cognitive capabilities, enabling machines to learn, reason, and provide predictive insights. This powerful combination not only automates processes but also empowers organizations with intelligent decision-making and operational agility. RPA and AI, while powerful individually, unlock unprecedented potential when combined. RPA excels at automating repetitive, rule-based tasks, reducing human error, and enhancing operational efficiency. In contrast, AI brings cognitive capabilities, enabling machines to learn, reason, and make data-driven decisions. Together, they create a synergistic solution that not only automates processes but also enhances them with intelligence, paving the way for smarter decision-making and more agile business operations. This white paper examines the integration of RPA and AI, highlighting their synergistic potential to transform business operations. It explores key use cases across industries, including finance, customer service, healthcare, and supply chain management. Additionally, the paper addresses challenges such as legacy system integration, data security, and change management, offering strategic recommendations for successful implementation. By leveraging Intelligent Automation, organizations can achieve scalability, efficiency, and a competitive edge in the digital age.

The financial services industry is increasingly adopting innovative technologies to tackle rising challenges in fraud prevention and operational efficiency. The integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) presents a promising solution to address these issues. AI, with its advanced capabilities in machine learning, data analytics, and pattern recognition, is particularly effective in detecting fraudulent activities in real-time. On the other hand, RPA facilitates the automation of repetitive and time-consuming tasks, allowing organizations to reduce operational inefficiencies and improve overall service delivery. This paper explores how the integration of AI and RPA enhances fraud prevention and boosts operational efficiency in financial services. The research draws from a mixed-methods approach, combining a comprehensive literature review, case studies from major financial institutions, and interviews with industry professionals. The findings reveal that the combination of AI’s fraud detection capabilities and RPA’s automation of back-office tasks leads to a significant reduction in fraudulent activities and operational costs. Financial institutions that adopted both technologies reported up to a 40% reduction in transaction processing time and a 25% decrease in operational expenses. The paper concludes that integrating AI and RPA not only transforms the way financial institutions manage risks and optimize operations but also sets the stage for future advancements in security and efficiency. This research offers valuable insights into the practical implications of these technologies, which can help guide financial institutions in adopting effective strategies for long-term success.

This article investigates the convergence of Artificial Intelligence (AI) and Robotic Process Automation (RPA) as a hybrid approach to overcome current limitations in automated processing of unstructured, non-routine business tasks. While traditional RPA excels at rule-based, repetitive processes, it struggles with the ambiguity and complexity inherent in decision-intensive workflows. Through a methodological framework combining theoretical analysis and empirical case studies across multiple industries, this article examines how AI technologies—specifically natural language processing, computer vision, and cognitive computing—can be architecturally integrated with RPA to create more adaptable and intelligent automation systems. The article identifies key integration patterns, implementation challenges, and organizational considerations for successful deployment of hybrid AI-RPA solutions. Findings suggest that properly orchestrated AI-RPA systems demonstrate significant capabilities in handling complex document processing, contextual decision-making, and exception management that neither technology could effectively address independently. The article contributes both theoretical insights into the evolution of intelligent automation and practical guidance for organizations seeking to extend automation beyond structured processes into knowledge-intensive domains.

A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives

Artificial intelligence and robotic process automation are among the next generation of digital technologies that are transforming the workplace and our daily lives. And digital transformation has become a critical strategy for many businesses and their leadership. In today's fast-paced and ever-changing business environment, the cornerstone of organizational success is innovation. As the business environment is in perpetual modification and development, it requires businesses to be adaptive to both external and internal factors. The instability of the organizational environment increases the necessity and benefits of employee innovation. The authors analyze the extensive literature defining artificial intelligence, focusing on automation and specifically the role robotic process automation has in increasing organizational efficiency, reducing cost, and ensuring quality. In expanding on the scientific literature surrounding robotic process automation, the article analyses a case study of a Romanian tech company whose complex internal technology ecosystem has resulted in a disconnected landscape which required constant multi-system manual intervention in order to ensure process and data cohesion. The authors present the company’s response to the increased consumption of human capital on repetitive tasks that were increasingly subject to errors, in the form of robotic process automation and levels of machine learning and artificial intelligence. In following the progress of the company towards adopting robotic process automation to increase efficiency, speed, accuracy, and security of processes and tasks, the authors identify the benefits of directing human labor towards more meaningful activities and allowing the repetitive tasks to be shifted to robotic labor. The authors followed the progress of a service technology solutions organization that has established an IT intelligent operation ecosystem, with new strategies across various key departments such as finance, human resources, and Internal Audit, followed by their leadership perspective and thinking for the company's next steps in this case study.

Pricing strategies are of paramount importance in the fiercely competitive retail sector, exerting a substantial influence on a company's financial performance and market standing. The amalgamation of artificial intelligence (AI) and robotic process automation (RPA) presents merchants with a potentially revolutionary opportunity to include and augment their pricing strategies via automation. The present research article investigates the field of AI-enhanced Robotic Process Automation (RPA) within the realm of retail pricing. It aims to analyses the impact of RPA on decision-making processes, operational efficiency, and overall organizational success. This research offers a thorough examination of pertinent scholarly works, empirical examples, and theoretical frameworks to investigate the advantages, challenges, and potential future trajectories associated with the utilization of artificial intelligence (AI) and robotic process automation (RPA) to augment pricing strategies in the retail sector.

Artificial Intelligence (AI) is steadily becoming the new normal in doing business through increasing operational performance, improving customer relations, and increasing predictive accuracy. This quantitative exploratory research employed a mixed-methods approach, integrating qualitative insights into organizational trends, best practices, and challenges with quantitative assessments of performance measures, cost savings, and business outcomes. Several surveys were administered to a diverse group of business professionals. The study, situated within the field of applied research, explores how AI facilitates business growth through change and proposes best practices for successful integration. It also studies what happens during transition periods when organizations emphasize artificial intelligence, NLP, and robotic process automation as top technologies since they contribute to completing work tasks, analyzing large data sets, and improving individual communication with clients. Besides potentially generated cost savings and a long-term increase in business value, there are several obstacles that organizations face if implementing AI, such as high initial costs and a market that requires professional knowledge on the topic. If implemented correctly, AI technologies hold huge potential for businesses going through transitions and taking advantage of AI’s strengths. For this reason, it is important to describe and analyze trends like AI technology integration accurately. This article suggests best practices for applying AI in business development during transformations. Keywords: Artificial intelligence, machine learning, business transitions, predictive analytics, robotic process automation, cost reduction, AI adoption strategies

Purpose: The main objective of the article is to identify selected ICT technologies supporting higher levels of organizational process maturity. Design/methodology/approach: The research was conducted with the use of the following methods: bibliometric analysis, literature review and statistic methods. The empirical procedure was carried out on a non-random sample of 48 large organizations operating in Poland, using the CAWI technique. Findings: The empirical research carried out proved the existence, in the group of the organizations examined, of a statistically significant relationship between the implementation of artificial intelligence (AI), cloud computing (CC) and robotic process automation (RPA) technologies and, respectively, the third and fourth levels of process maturity, in accordance with the adopted multicriteria model of process maturity assessment (MMPM). Research limitations/implications: The burden of the presented empirical investigation results primarily arises from the applied technique of non-probabilistic research sample selection. This makes the obtained results limited to the examined sample of organizations. Originality/value: The originality of this article fills the cognitive gap consisting in the shortage of publications that present the relationship between the degree of implementation of ICT technology and the level of process maturity. The presented article addresses this gap by indicating a statistical relationship between the implementation of artificial intelligence (AI), robotic process automation (RPA), cloud computing (CC) technology and the level of process maturity of the organization.

The impact of digital transformation is spreading across global marketplaces and sectors. Blockchain, robotic process automation (RPA), and artificial intelligence (AI) have a huge amount of potential to deal with every sector. Blockchain, RPA, and AI are seeing increasing adoption across multiple market segments. The integration of technologies offers the possibility to produce more intelligent, secure, efficient, and safe systems than those that already exist. Innovative technologies are improving responsiveness in terms of intellectual thinking and seeing within the limits of time act. This chapter gives a brief explanation of the integration of blockchain and RPA and how it is beneficial for digital finance and the business sector. Blockchain has the power to store massive data and it is interesting to see what are the methods and systems in which blockchain is used with artificial intelligence and robotic process automation. It can be said that shortly blockchain technology will be used in different domains of the technology sector with the help of AI and RPA.