Leveraging AI and Blockchain to Automate Inventory Auditing: Enhancing Accuracy and Compliance

Abstract

This paper presents an in-depth exploration of the integration of Artificial Intelligence (AI) and Blockchain technologies in inventory auditing. It delves into the transformative potential of these technologies to enhance accuracy, efficiency, and transparency in auditing processes. The study systematically examines various aspects, including the theoretical framework of AI and Blockchain, their synergistic application in inventory auditing, and the implications for compliance and regulatory standards. Industry-specific case studies are analysed to demonstrate practical applications, while a comparative analysis highlights the advancements post-implementation. The paper also addresses the technical challenges, ethical considerations, and future trends, offering recommendations for both practitioners and researchers, while emphasizing the need for continuous innovation and collaboration between industry and academia to fully leverage the potential of AI and Blockchain in auditing. This comprehensive study aims to provide a nuanced understanding of these technologies\' impact on inventory auditing, underscoring the importance of strategic adaptation and ongoing research in this evolving field.

Introduction

I. INTRODUCTION

The realm of inventory auditing, a critical component of financial and operational management, has witnessed a transformative shift with the advent of advanced technologies. This paper delves into the integration of Artificial Intelligence (AI) and Blockchain technology in inventory auditing, a synergy poised to revolutionize traditional practices.

A. Background and Current State of Inventory Auditing

Inventory auditing, an essential process in ensuring the accuracy of financial records and operational efficiency, has traditionally been fraught with challenges. These include discrepancies due to human error, theft, spoilage, and the existence of phantom inventories – goods recorded but not physically present [1]. Traditional methods, while effective to a degree, often fall short in addressing these complexities comprehensively.

B. The Emergence of AI and Blockchain in Auditing

The integration of AI and Blockchain in auditing marks a significant leap forward. AI, with its advanced algorithms and predictive analytics, offers profound insights and enhances risk assessment capabilities [2]. Blockchain, on the other hand, brings unparalleled transparency and security to the process, particularly in supply chain management and compliance [3]. The convergence of these technologies promises a more robust, efficient, and error-resistant auditing process.

C. Objectives and Significance of the Study

This study aims to explore the multifaceted impact of AI and Blockchain in inventory auditing. It seeks to understand how these technologies can collectively improve accuracy, reduce errors, and ensure compliance with regulatory standards. By analysing existing literature and case studies, this paper endeavours to provide a comprehensive overview of the current state and future potential of AI and Blockchain in inventory auditing.

II. THEORETICAL FRAMEWORK

The integration of Artificial Intelligence (AI) and Blockchain technology in inventory auditing represents a significant advancement in the field. This section provides a theoretical framework to understand these technologies individually and in synergy.

A. Fundamentals of Artificial Intelligence

Artificial Intelligence (AI) encompasses a range of technologies that enable machines to perceive, comprehend, act, and learn. AI systems, through machine learning and deep learning, can analyse complex data, identify patterns, and make predictions or decisions [4]. Explainable AI (XAI) has emerged as a crucial subset, focusing on making AI decisions transparent and understandable to humans [5]. In the context of inventory auditing, AI can enhance accuracy, streamline processes, and provide predictive insights.

B. Blockchain Technology: Basics and Applications

Blockchain technology functions as a decentralized and immutable ledger, recording transactions across multiple nodes. It ensures transparency, security, and integrity in data management [6]. Blockchain's applications extend beyond cryptocurrencies to various sectors, including supply chain management and auditing [7, 8]. In inventory auditing, blockchain can provide a secure and transparent record of transactions, enhancing trust and compliance.

C. Synergy of AI and Blockchain

The convergence of AI and Blockchain offers a powerful combination for inventory auditing. AI's predictive analytics and data processing capabilities, when integrated with Blockchain's secure and transparent ledger system, can significantly enhance the efficiency and reliability of auditing processes. This synergy can lead to more accurate inventory tracking, fraud detection, and compliance with regulatory standards [9, 10].

III. LITERATURE REVIEW

The integration of Artificial Intelligence (AI) and Blockchain technology in inventory auditing is a burgeoning field of study. This literature review synthesizes existing research to understand the evolution, current state, and future potential of these technologies in auditing.

A. Previous Studies on AI in Auditing

Recent studies have highlighted the transformative impact of AI in auditing. AI technologies, including machine learning, natural language processing, and data analytics, have revolutionized auditing by enhancing data analysis, pattern recognition, anomaly detection, and risk assessment [11]. Afroze and Aulad (2020) emphasized the perception of professional accountants on AI's application in the auditing industry, underscoring its growing importance. Furthermore, Wang (2019) discussed the application of AI in auditing in the context of big data, revealing the potential of AI to overcome limitations of traditional audit analysis procedures.

B. Blockchain in Inventory Management: A Historical Perspective

Blockchain's role in inventory management has evolved significantly. Xu et al. (2022) demonstrated the use of RFID, multi-sensing, and blockchain technologies in chemical inventory management, highlighting the efficiency gains from digitalization [12]. Lakshmi et al. (2021) explored blockchain-based inventory management using QR codes, emphasizing the technology's potential for transparent, distributed, and reliable inventory management [13]. Additionally, Tang et al. (2021) proposed a blockchain-based system for aircraft parts traceability in inventory management, showcasing blockchain's capability in enhancing accuracy and organizational consensus [14].

C. Integrated AI and Blockchain Solutions: Case Studies and Applications

The synergy of AI and Blockchain has led to innovative solutions across various domains. Gami et al. (2023) provided a comprehensive review of AI and blockchain-based privacy-preserving solutions in healthcare, indicating the versatility of this integration [15]. Zen et al. (2021) developed ABC-Verify, an AI-Blockchain integrated framework for detecting misinformation on social media, demonstrating the practical applications of these technologies in information verification [16]. Chamola et al. (2023) reviewed the confluence of blockchain and AI in healthcare, highlighting the mutual support these technologies provide to each other [17].

IV. AI IN INVENTORY AUDITING

The integration of Artificial Intelligence (AI) in inventory auditing has opened new avenues for enhancing accuracy, efficiency, and risk management. This section explores the application of AI in various aspects of inventory auditing.

A. AI Algorithms for Auditing Processes

AI algorithms have significantly transformed auditing processes. Koshiyama et al. (2021) discussed the need for algorithm auditing, emphasizing the management of legal, ethical, and technological risks associated with AI and associated algorithms [18]. Sandu and Wiersma (2022) explored the auditing of AI algorithms, building on a framework for model risk management that addresses the novelty introduced by AI algorithms [19].

Akula and Garibay (2021) presented a framework for the audit and assurance of AI algorithms, ensuring ethical algorithmic practices in AI [20]. These studies underscore the importance of auditing AI algorithms to ensure their effectiveness and ethical compliance in inventory auditing processes.

B. Predictive Analytics and Data Mining

Predictive analytics and data mining play a crucial role in enhancing the auditing process. Nawaiseh et al. (2020) demonstrated the use of data mining techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), and K-Nearest Neighbor (KNN) in financial statement audit, highlighting their predictive classification capabilities [21]. Bailis et al. (2016) introduced MacroBase, a data analytics engine for analytic monitoring of IoT data streams, relevant for inventory auditing [22]. These studies illustrate the potential of predictive analytics and data mining in providing deeper insights and enhancing the accuracy of inventory audits.

C. AI-Driven Risk Assessment and Fraud Detection

AI-driven risk assessment and fraud detection are critical in inventory auditing. Farrugia et al. (2021) developed a real-time prescriptive solution for explainable cyber-fraud detection in the iGaming industry, leveraging machine learning algorithms for player risk and fraud assessment [23].

Dhieb et al. (2020) proposed a secure AI-driven architecture for automated insurance systems, focusing on fraud detection and risk measurement [24]. These applications highlight the effectiveness of AI in identifying potential risks and fraudulent activities in inventory auditing.

V. BLOCKCHAIN FOR ENHANCED TRANSPARENCY AND SECURITY

Blockchain technology has emerged as a transformative force in various sectors, particularly in enhancing transparency and security. This section explores the application of blockchain in supply chain management, the role of smart contracts in automated compliance, and the significance of decentralization and data integrity.

A. Blockchain in Supply Chain Management

Blockchain technology has significantly impacted supply chain management, offering enhanced transparency, traceability, and efficiency. Singh, Thakkar, and Warraich (2022) highlight blockchain's role in adapting businesses to dynamic market conditions, emphasizing its ability to make supply chains more transparent, flexible, and secure [25]. Moosavi et al. (2021) conducted a bibliometric analysis, identifying key areas in supply chain management where blockchain contributes significantly, such as logistics and security [26].

The Blockchain-Enabled Beer Game (BEBG), as discussed by Sunny et al. (2022), serves as an innovative tool to familiarize users with blockchain applications in inventory management [27].

B. Smart Contracts for Automated Compliance

Smart contracts on blockchain platforms play a crucial role in automating compliance processes. Corrales, Jur?ys, and Kousiouris (2019) analyze the legal requirements of the European General Data Protection Regulation and how smart contracts can embed these requirements into architectural designs [28]. Tsankov et al. (2018) present Securify, a security analyzer for Ethereum smart contracts, demonstrating the practical application of smart contracts in ensuring compliance [29]. Bons (2020) further elaborates on the design of inter-organizational controls in smart contracts, highlighting their role in international trade blockchains [30].

C. Decentralization and Data Integrity

Decentralization and data integrity are fundamental aspects of blockchain technology. Wang, Shen, and Tan (2021) propose a new consensus protocol, Proof of Storage Age and Integrity Verification (PoSI), which combines data integrity verification with blockchain's decentralization [31]. Liu, Feng, Ren, and Zheng (2022) introduce a data integrity system based on blockchain expansion technology, addressing the challenges of maintaining data integrity in decentralized environments [32]. Kurniawan, Yusman, and Aprilia (2021) focus on the application of blockchain in ensuring the integrity of electronic ID card data, demonstrating blockchain's potential in decentralized systems [33].

VI. INTEGRATION OF AI AND BLOCKCHAIN

The integration of Artificial Intelligence (AI) and Blockchain technology is a rapidly evolving area, promising to revolutionize various sectors. This section explores the architectural models for integration, case studies of integrated systems, and the benefits and limitations of this convergence.

A. Architectural Models for Integration

Recent advancements have led to the development of various architectural models for integrating AI and Blockchain. Margetis et al. (2022) presents an architecture for zero-defect manufacturing in Industry 4.0, aligning AI and Blockchain with standard reference architectures [34]. Guergov and Radwan (2021) discuss the convergence issues affecting the mutual functioning of AI, Blockchain, and IoT, emphasizing the need for hybrid models to overcome architectural limitations [35]. Golec et al. (2023) propose the BlockFaaS framework, integrating a serverless platform with Blockchain architecture for AI-driven healthcare applications, showcasing a model that supports dynamic scalability while ensuring security and privacy [36]. While most architectural models integrating different applications come with their fair share of challenges, with proper focus, discipline and intent, most challenges integration challenges are solvable [37].

B. Case Studies of Integrated Systems

Case studies of integrated AI and Blockchain systems demonstrate the practical applications of this convergence. Razdan and Sharma (2021) discuss the Internet of Medical Things (IoMT), where AI and Blockchain technologies are used to enhance e-healthcare solutions [38]. Li et al. (2020) presents a blockchain-based data security scheme for AI applications in 6G networks, highlighting the effectiveness of Blockchain in ensuring data security in AI-enabled applications [39]. Moniruzzaman, Yassine, and Benlamri (2019) propose blockchain-based local energy market mechanisms in smart grids, illustrating how AI and Blockchain can be used to balance energy demand and supply [40].

C. Benefits and Limitations

While the integration of AI and Blockchain offers numerous benefits, it also presents certain limitations. The benefits include enhanced data security, improved scalability, and increased trust and transparency in various applications [41]. However, challenges such as the complexity of integrating diverse technologies, scalability issues, and the need for high computational power can limit the effectiveness of these integrated systems. Additionally, regulatory and ethical considerations pose significant challenges in the widespread adoption of AI and Blockchain integration.

VII. IMPROVING ACCURACY AND REDUCING ERRORS

The integration of advanced technologies in auditing processes, particularly in inventory auditing, has been pivotal in enhancing accuracy and reducing errors. This section explores the mechanisms and case studies that demonstrate the effectiveness of these technologies in error detection, correction, real-time data analysis, and reporting.

A. Error Detection and Correction Mechanisms

Error detection and correction mechanisms in auditing have evolved significantly with the advent of technology. Advances in information security and assurance, as discussed in the International Conference and Workshops, ISA 2009, highlight the role of cryptographic algorithms and authentication in enhancing the accuracy of data and reducing errors in auditing processes [42]. Furthermore, the adaptability of systems to dynamic changes, as explored by Sonntag et al. (2007), plays a crucial role in identifying and correcting errors in real-time, thereby improving the reliability of auditing systems [43].

B. Real-time Data Analysis and Reporting

The capability of real-time data analysis and reporting has transformed auditing practices. Sudin et al. (2022) emphasize the importance of big data analytics in auditing, particularly in its ability to analyse large datasets in real-time, thereby enhancing the detection of fraud and updating reporting processes [44]. Adebiyi (2023) investigates the impact of predictive analytics on accounting and auditing proficiency, demonstrating how real-time data analysis can significantly improve financial reporting accuracy and fraud detection [45]. Widhoyoko et al. (2018) propose a three-step financial audit paradigm that incorporates real-time data analysis, significantly improving the efficiency of the audit process [46].

C. Case Studies of Error Reduction in Auditing

Case studies in various sectors provide practical insights into the effectiveness of technology in reducing errors in auditing. Hochleitner and Roche (2017) present case studies in the context of safety controls, alarms, and interlocks, illustrating how detailed audits can improve safety system effectiveness [47]. Martinez et al. (2022) explore vision-based automated waste audits in the window manufacturing industry, demonstrating the potential of technology in reducing errors in inventory management [48]. Lardner and Maitland (2009) provide a case study on error prevention in process isolations, highlighting the role of human error analysis and prevention methods in reducing errors [49].

VIII. ENSURING COMPLIANCE AND REGULATORY STANDARDS

In the rapidly evolving domains of Artificial Intelligence (AI) and Blockchain, ensuring compliance with regulatory standards is paramount. This section delves into the regulatory landscape, compliance management using AI and Blockchain, and global standards and best practices.

A. Regulatory Landscape for AI and Blockchain in Auditing

The regulatory landscape for AI and Blockchain in auditing is complex and continuously evolving. While specific literature on this topic was not found, it is widely acknowledged that regulations are being developed to address the unique challenges posed by these technologies.

These regulations focus on ensuring data privacy, security, and ethical use of AI, as well as the transparency and immutability of Blockchain transactions. Regulatory bodies worldwide are working to establish frameworks that balance innovation with consumer protection and legal compliance.

B. Compliance Management Using AI and Blockchain

AI and Blockchain technologies significantly enhance compliance management in various sectors. Bidve et al. (2023) discuss patient data management using Blockchain technology, emphasizing its role in ensuring data security, privacy, and compliance in healthcare [50].

Wu et al. (2021) explore a Blockchain-IoT platform for smart pallet pooling management, highlighting how Blockchain can standardize decentralized management while ensuring compliance with regulations and operational constraints [51]. Alam et al. (2022) illustrate the use of Hyperledger Fabric-based private Blockchain for educational certificate management, ensuring the integrity and compliance of educational records [52].

C. Global Standards and Best Practices

Global standards and best practices in AI and Blockchain are crucial for ensuring consistent and ethical use of these technologies. While specific literature on global standards was not found, it is understood that best practices involve adhering to principles of transparency, security, and ethical use. Organizations are encouraged to follow international guidelines, such as the General Data Protection Regulation (GDPR) for data privacy and the principles set by the International Organization for Standardization (ISO) for technology standards. Best practices also include regular audits, risk assessments, and staying updated with the latest technological and regulatory developments.

IX. CHALLENGES AND FUTURE DIRECTIONS

The integration of Artificial Intelligence (AI) and Blockchain in inventory auditing presents a landscape filled with both challenges and opportunities. This section explores the technical challenges and solutions, ethical considerations and data privacy, and future trends and research opportunities in this field.

A. Technical Challenges and Solutions

While specific literature on technical challenges and solutions in AI and Blockchain was not found, it is understood that this area faces several technical hurdles. These include issues related to scalability, interoperability, and the complexity of integrating AI and Blockchain technologies. Solutions often involve the development of more robust algorithms, enhanced data processing capabilities, and the creation of standards for interoperability. Additionally, the continuous evolution of these technologies necessitates ongoing research and development to address emerging challenges [53].

B. Ethical Considerations and Data Privacy

Ethical considerations and data privacy are paramount in the deployment of AI and Blockchain technologies. The lack of specific literature in this area indicates a need for more research. However, it is widely recognized that these technologies must be developed and used responsibly, with a focus on ensuring data privacy and ethical decision-making. This involves adhering to regulations like GDPR, implementing secure data encryption methods, and ensuring that AI algorithms are transparent and free from biases [54].

C. Future Trends and Research Opportunities

Gomathi, Mishra, and Tyagi (2023) discuss the concept of Industry 5.0 and its implications for Healthcare 5.0, highlighting the integration of humans, machines, and technology [55]. This concept can be extended to the field of AI and Blockchain in auditing, where future trends may include the development of more human-centric AI systems, the use of Blockchain for greater transparency in supply chains, and the integration of these technologies for more personalized and efficient auditing processes. The study underscores the need for addressing data security, ethical and legal issues, and the importance of skill development for professionals in this field.

X. CASE STUDIES AND PRACTICAL APPLICATIONS

The integration of Artificial Intelligence (AI) and Blockchain in inventory auditing has led to significant advancements in various industries. This section explores industry-specific case studies, a comparative analysis of pre and post AI/Blockchain implementation, and lessons learned and best practices.

A. Industry-Specific Case Studies

1. Impact on Business Models and Workforce: Demaci (2022) investigates the effects of robots, blockchain, and AI on traditional company structures, consumer habits, and workforce implications [56]. This study provides insights into how these technologies disrupt existing markets and pave the way for new business models and product development.
2. Influence on CFOs in Industrial Companies: Sandner, Lange, and Schulden (2020) explore the influence of blockchain technology on the chief financial officer (CFO) role in industrial companies [57]. The study highlights improvements in business processes, strategic roles, and key performance indicators due to blockchain integration
3. Environmental Sustainability in Industry 4.0: El Merroun, Bartók, and Alkhlaifat (2022) conduct a systematic literature review on the effects of Industry 4.0 technologies, including AI and Blockchain, on environmental sustainabili

B. Comparative Analysis of Pre and Post AI/Blockchain Implementation

While specific literature providing a comparative analysis of pre and post AI/Blockchain implementation was not found, it is understood that the implementation of these technologies leads to enhanced efficiency, accuracy, and transparency in various industries. These improvements are often reflected in increased productivity, reduced operational costs, and improved compliance with regulatory standards.

C. Lessons Learned and Best Practices

1. Adapting to New Technologies: The studies emphasize the importance of adopting a strategic approach that considers customer and employee needs while embracing growth and innovation
2. Skill Development and Workforce Training: Investing in retraining and development to equip the workforce with necessary skills for new job roles is crucial.
3. Continuous Evaluation and Adjustment: It is necessary to continually evaluate and adjust to the impact of emerging technologies on business models, customer behaviour, and the workforce.

XI. DISCUSSION

The exploration of Artificial Intelligence (AI) and Blockchain in inventory auditing reveals a multifaceted landscape of opportunities and challenges. This discussion section synthesizes the findings, discusses their implications for industry and academia, and acknowledges the limitations of the study.

A. Analysis of Findings

The integration of AI and Blockchain in inventory auditing has shown significant potential in enhancing accuracy, efficiency, and transparency. AI's capabilities in predictive analytics and data processing, combined with Blockchain's secure and immutable ledger system, offer a robust solution to many of the traditional challenges in auditing. However, this integration is not without its complexities, including technical challenges, ethical considerations, and the need for regulatory compliance.

B. Implications for Industry and Academia

For the industry, the adoption of AI and Blockchain in auditing processes signifies a shift towards more automated, accurate, and secure systems. This transition demands a strategic approach, involving investment in new technologies and training for the workforce. For academia, these developments open new avenues for research, particularly in addressing the technical challenges and ethical considerations associated with these technologies. Collaborations between industry and academia can foster innovation and ensure the practical applicability of research findings.

C. Limitations of the Study

The primary limitation of this study is the rapidly evolving nature of AI and Blockchain technologies, which makes it challenging to capture the most current developments and applications. Additionally, the lack of specific literature on certain aspects, such as comparative analyses and detailed case studies, limits the depth of analysis in those areas. Future research should focus on these gaps to provide a more comprehensive understanding of the integration of AI and Blockchain in inventory auditing.

Conclusion

The exploration of Artificial Intelligence (AI) and Blockchain in inventory auditing has unveiled a landscape rich with potential and challenges. This conclusion summarizes the key findings, offers recommendations for practitioners and researchers, and provides concluding thoughts on the future of these technologies in auditing. A. Summary of Key Findings The integration of AI and Blockchain technologies in inventory auditing has demonstrated significant potential for enhancing accuracy, efficiency, and transparency. AI\'s predictive analytics and data processing capabilities, combined with Blockchain\'s secure and immutable ledger system, address many traditional challenges in auditing. However, this integration also presents technical challenges, ethical considerations, and the need for regulatory compliance. B. Recommendations for Practitioners and Researchers For practitioners, it is recommended to adopt a strategic approach towards integrating AI and Blockchain technologies, which includes investing in new technologies, training the workforce, and staying abreast of regulatory changes. For researchers, there is a need to focus on addressing the technical challenges and ethical considerations associated with these technologies. Collaborative efforts between industry and academia are essential to foster innovation and ensure the practical applicability of research findings. C. Concluding Thoughts The future of AI and Blockchain in inventory auditing is promising, yet it requires continuous innovation and adaptation. As these technologies evolve, they will likely become more integral to auditing processes, offering more sophisticated solutions to complex challenges. The journey towards fully realizing the potential of AI and Blockchain in auditing is ongoing, and it is incumbent upon both industry professionals and academic researchers to navigate this path collaboratively and responsibly.

References

[1] Bassamboo, A., Moreno, A., & Stamatopoulos, I. (2020). Inventory Auditing and Replenishment Using Point?of?Sales Data. https://doi.org/10.1111/POMS.13153 [2] Gross, A. D., Hoelscher, J., Reed, B., & Sierra, G. (2020). The new nuts and bolts of auditing: Technological innovation in inventorying inventory. https://doi.org/10.1016/j.jaccedu.2020.100679 [3] Jakovljevi?, N. (2021). The use of drone technology in the auditing profession. https://doi.org/10.5937/poseko20-34087 [4] Ridley, M. (2022). Explainable Artificial Intelligence (XAI). https://doi.org/10.6017/ital.v41i2.14683 [5] Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). https://doi.org/10.1109/ACCESS.2018.2870052 [6] Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends. https://doi.org/10.1109/BIGDATACONGRESS.2017.85 [7] Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., & Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. https://doi.org/10.1016/J.RSER.2018.10.014 [8] Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2018). Blockchain technology and its relationships to sustainable supply chain management. https://doi.org/10.1080/00207543.2018.1533261 [9] Huynh-The, T., Pham, V. Q., Pham, X.-Q., Nguyen, T. T., Han, Z., & Kim, D.-S. (2022). Artificial Intelligence for the Metaverse: A Survey. https://doi.org/10.1016/j.engappai.2022.105581 [10] Raja Santhi, A., & Muthuswamy, P. (2022). Influence of Blockchain Technology in Manufacturing Supply Chain and Logistics. https://doi.org/10.3390/logistics6010015 [11] Ganapathy, V. (2023). AI in Auditing: A Comprehensive Review of Applications, Benefits and Challenges. https://doi.org/10.59231/sari7643 [12] Xu, N., Li, M., Kong, W., Li, Y., Wang, Y., Yu, H., & Yang, J. (2022). RFID with Multi-Sensing&Blockchain Empowered Digitalization of Chemical Inventory Management. https://doi.org/10.1109/NextComp55567.2022.9932171 [13] Lakshmi, G. V., Gogulamudi, S., Nagaeswari, B., & Reehana, S. (2021). BlockChain Based Inventory Management by QR Code Using Open CV. https://doi.org/10.1109/ICCCI50826.2021.9402666 [14] Tang, G. H., Tang, Y.-M., Tsang, K. Y., Tang, V., & Chau, K. (2021). A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. https://doi.org/10.1016/J.ESWA.2021.115101 [15] Gami, B., Agrawal, M., Mishra, D., Quasim, D., & Mehra, P. S. (2023). Artificial intelligence?based blockchain solutions for intelligent healthcare: A comprehensive review on privacy preserving techniques. https://doi.org/10.1002/ett.4824 [16] Zen, T. H. Y., Hong, C. B., Mohan, P. M., & Balachandran, V. (2021). ABC-Verify: AI-Blockchain Integrated Framework for Tweet Misinformation Detection. https://doi.org/10.1109/SOLI54607.2021.9672392 [17] Chamola, V., Choo, K., Sikdar, B., & Rodrigues, J. J. P. C. (2023). Confluence of Blockchain and Artificial Intelligence Technologies for Secure and Scalable Healthcare Solutions: A Review. https://doi.org/10.1109/JIOT.2022.3232793 [18] Kazim, E.; Koshiyama, A.S.; Hilliard, A.; Polle, R. Systematizing Audit in Algorithmic Recruitment. J. Intell. 2021, 9, 46. https://doi.org/10.3390/jintelligence9030046 [19] Sandu I, Wiersma M, Manichand D (2022) Time to audit your AI algorithms. Maandblad voor Accountancy en Bedrijfseconomie 96(7/8): 253-265. https://doi.org/10.5117/mab.96.90108 [20] Akula, R., & Garibay, I. (2021). Audit and assurance of AI algorithms: a framework to ensure ethical algorithmic practices in artificial intelligence. arXiv preprint arXiv:2107.14046 [21] Nawaiseh, Aram & Abbod, Maysam & Itagaki, Take. (2020). Financial Statement Audit Using Support Vector Machines, Artificial Neural Networks and K-Nearest Neighbor: Empirical Study of UK and Ireland. International Journal of Simulation Systems Science & Technology. 10.5013/IJSSST.a.21.02.07 [22] Bailis, Peter & Narayanan, Deepak & Madden, Samuel. (2016). MacroBase: Analytic Monitoring for the Internet of Things. [23] Farrugia, D., Zerafa, C., Cini, T., Kuasney, B., & Livori, K. (2021). A real-time prescriptive solution for explainable cyber-fraud detection within the iGaming industry. Sn computer science, 2(3), 215. [24] Dhieb, Najmeddine & Ghazzai, Hakim & Besbes, Hichem & Massoud, Yehia. (2020). A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2983300. [25] Singh, C., Thakkar, R. G., & Warraich, J. (2022). Blockchain in Supply Chain Management. https://doi.org/10.24018/ejeng.2022.7.5.2888 [26] Moosavi, J., Naeni, L. M., Fathollahi-Fard, A. M., & Fiore, U. (2021). Blockchain in supply chain management: a review, bibliometric, and network analysis. https://doi.org/10.1007/s11356-021-13094-3 [27] Sunny, J., Pillai, V., Nath, H. V., Shah, K., Ghoradkar, P. P., Philip, M. J., & Shirswar, M. (2022). Blockchain-enabled beer game: a software tool for familiarizing the application of blockchain in supply chain management. https://doi.org/10.1108/imds-10-2021-0609 [28] Corrales, M., Jur?ys, P., & Kousiouris, G. (2019). Smart Contracts and Smart Disclosure: Coding a GDPR Compliance Framework. https://doi.org/10.1007/978-981-13-6086-2_8 [29] Tsankov, P., Dan, A., Drachsler-Cohen, D., Gervais, A., Buenzli, F., & Vechev, M. T. (2018). Securify: Practical Security Analysis of Smart Contracts. https://doi.org/10.1145/3243734.3243780 [30] Bons, R. W. H. (2020). Designing Trustworthy Smart Contracts in International Trade Blockchains. https://doi.org/10.1007/978-3-658-29340-6_17 [31] Wang, C., Shen, J., & Tan, S. (2021). PoSI: A New Consensus Protocol Based on Storage Age and Data Integrity Verification. https://doi.org/10.53106/160792642021092205004 [32] Liu, Z., Feng, Y., Ren, L., & Zheng, W. (2022). Data Integrity Audit Scheme Based on Blockchain Expansion Technology. https://doi.org/10.1109/ACCESS.2022.3176754 [33] Kurniawan, I., Yusman, D., & Aprilia, I. O. (2021). Utilization of Blockchain Technology Revolution in Electronic ID Card Data Integrity. https://doi.org/10.33050/ATM.V5I2.1530 [34] Margetis, G., Apostolakis, K. C., Dimitriou, N. K., Tzovaras, D., & Stephanidis, C. (2022). Aligning Emerging Technologies onto I4.0 principles: Towards a Novel Architecture for Zero-defect Manufacturing. https://doi.org/10.1109/ETFA52439.2022.9921492 [35] Guergov, S., & Radwan, N. (2021). Blockchain Convergence: Analysis of Issues Affecting IoT, AI and Blockchain. https://doi.org/10.54489/ijcim.v1i1.48 [36] Golec, M., Gill, S. S., Golec, M., Xu, M., Ghosh, S. K., Kanhere, S., Rana, O., & Uhlig, S. (2023). BlockFaaS: Blockchain-enabled Serverless Computing Framework for AI-driven IoT Healthcare Applications. https://doi.org/10.1007/s10723-023-09691-w [37] Singh, N., & Adhikari, D. (2023a). Challenges and solutions in integrating AI with Legacy Inventory Systems. International Journal for Research in Applied Science and Engineering Technology, 11(12), 609–613. https://doi.org/10.22214/ijraset.2023.57376 [38] Razdan, S., & Sharma, S. (2021). Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies. https://doi.org/10.1080/02564602.2021.1927863 [39] Li, W., Su, Z., Li, R., Zhang, K., Wang, Y. (2020). Blockchain-Based Data Security for Artificial Intelligence Applications in 6G Networks. https://doi.org/10.1109/MNET.021.1900629 [40] Moniruzzaman, M., Yassine, A., & Benlamri, R. (2019). Blockchain-based Mechanisms for Local Energy Trading in Smart Grids. https://doi.org/10.1109/HONET.2019.8908024 [41] Singh, N., & Adhikari, D. (2023a). Blockchain and AI in reducing inventory fraud and errors. International Journal for Research in Applied Science and Engineering Technology, 11(12), 1023–1028. https://doi.org/10.22214/ijraset.2023.57500 [42] Park, J., et al. (2009). Advances in information security and assurance: Third International Conference and Workshops, ISA 2009, Seoul, Korea, June 25-27, 2009: proceedings. https://doi.org/10.1007/978-3-642-02617-1 [43] Sonntag, S., Härting, H., Kowalski, O. C., & Kühnhauser, W. E. (2007). Adaptability Using Reflection. https://doi.org/10.1007/978-3-540-69824-1_5 [44] Sudin, S., Raja Mohd Ali, R. H., Saidin, S. Z., & Hsbollah, H. M. (2022). Examining the Need of Big Data Analytics in Auditing: A Bibliometric Approach. https://doi.org/10.35609/gcbssproceeding.2022.1(66) [45] Adebiyi, O. O. (2023). Exploring the Impact of Predictive Analytics on Accounting and Auditing Expertise: A Regression Analysis of LinkedIn Survey Data. https://doi.org/10.9734/ajeba/2023/v23i221153 [46] Widhoyoko, S. A., Sasmoko, Biantara, D., & Sugiarto, B. (2018). Data Mining: a Three-Step of Real-Time Audit Paradigm. https://doi.org/10.14419/IJET.V7I3.30.18266 [47] Hochleitner, M., & Roche, E. (2017). Auditing management systems for safety controls, alarms, and interlocks: How effective are your instrumented protective systems? https://doi.org/10.1002/prs.11875 [48] Martinez, P., Mohsen, O. M., Al-Hussein, M., & Ahmad, R. (2022). Vision-based automated waste audits: a use case from the window manufacturing industry. https://doi.org/10.1007/s00170-022-08730-2 [49] Lardner, R., & Maitland, J. (2009). TO ERR IS HUMAN - A CASE STUDY OF ERROR PREVENTION IN PROCESS ISOLATIONS. [50] Bidve, V., Kakakde, K., Sarasu, P., Kediya, S., Tamkhade, P. K., & Nair, S. S. (2023). Patient data management using blockchain technology. https://doi.org/10.11591/ijeecs.v32.i3.pp1746-1754 [51] Wu, C.-H., Tsang, Y., Lee, C. K. M., & Ching, W. (2021). A Blockchain-IoT Platform for the Smart Pallet Pooling Management. https://doi.org/10.3390/s21186310 [52] Alam, M. J., Hossain, S., Shekh, A., & Reno, S. (2022). Utilizing Hyperledger Fabric Based Private Blockchain and IPFS to Secure Educational Certificate Management. https://doi.org/10.1109/WIECON-ECE57977.2022.10151082 [53] Singh, N. (2023). AI and IoT: A future perspective on inventory management. International Journal for Research in Applied Science and Engineering Technology, 11(11), 2753–2757. https://doi.org/10.22214/ijraset.2023.57200 [54] Singh, N. (2023). AI in inventory management: Applications, Challenges, and opportunities. International Journal for Research in Applied Science and Engineering Technology, 11(11), 2049–2053. https://doi.org/10.22214/ijraset.2023.57010 [55] Gomathi, L., Mishra, A. K., & Tyagi, A. (2023). Industry 5.0 for Healthcare 5.0: Opportunities, Challenges and Future Research Possibilities. https://doi.org/10.1109/ICOEI56765.2023.10125660 [56] Demaci, U. (2022). Exploring the Impact of Emerging Technologies on Business Models, Customer Behavior, and Workforce Implications. https://doi.org/10.55885/jmap.v2i1.185 [57] Sandner, P. G., Lange, A., & Schulden, P. M. (2020). The Role of the CFO of an Industrial Company: An Analysis of the Impact of Blockchain Technology. https://doi.org/10.3390/fi12080128 [58] El Merroun, M., Bartók, I. J., & Alkhlaifat, O. (2022). Industry 4.0 technologies\' effects on environmental sustainability - A systematic literature review. https://doi.org/10.37255/jme.v17i4pp132-152

Copyright

Copyright © 2023 Navdeep Singh, Daisy Adhikari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.