Advanced Digital Technologies in Financial and Business Management.pdf
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The success of the information technology industry is contingent on many advanced technologies and techniques. On a day-to-day basis, the industry is growing and developing new technologies. The technological implementation of these technologies in each and every sector has given a new platform to each and every business sector. A few technologies, namely artificial intelligence, machine learning, blockchain, and the Internet of Things, are implanted in the deep roots of business and financial services. This resource, Advanced Digital Technologies in Financial and Business Management: Unleashing the Power of Artificial Intelligence, Machine Learning, Blockchain, and the Internet of Things, explains the theoretical information along with the technical implementation of these technologies in finance and business services. This resource represents a pathway of these emerging technologies for the development of various business sectors and to strengthen economic growth. The amalgamation of these techniques and trends in the financial and business sectors develops and enhances the understanding, skills, and competence for planning and performing various types of assignments in these sectors. The main aim of this resource is to connect the different sectors with IoT, AI, Blockchain, ML, and other emerging technologies to create innovative business and finance management applications. The many academic areas covered in this resource include IoT, AI, blockchain, ML, and the recent development of these emerging technologies in the financial and accounting business sectors. This resource is ideally designed for computer professionals, researchers, academicians, student fraternities, etc., seeking knowledge and skills on AI, ML, blockchain, and IoT in financial and business management. This resource comprises 14 chapters describing the details of aforesaid technologies in various business sectors. Chapter 1: “Amalgamation of Artificial Intelligence (AI) and Data Science in Taxation” demonstrates the significance of combining AI and data science in taxation. This chapter also explains AI, the use of AI in tax administration, the expansion of tax collection, etc. Chapter 2: “A Retrospection of Business Intelligence in the Era of Big Data Analytics and Artificial Intelligence for Fintech” presents and investigates the importance of employing big data analytics (BDA), data mining, and AI while establishing and refining existing business intelligence in order to handle the problem that has been identified. In addition, the opportunities and challenges related to the development of value from data via the implementation of modern business intelligence techniques in the context of Fintech are also discussed. Chapter 3: “From Data to Insights: How AI and IoT are Reshaping Financial Services” focuses on the amalgamation of AI and IoT in the financial industry, discussing its evolution, use cases, and benefits, along with a little briefing about the contribution of AI and IoT as individual emerging technologies. Chapter 4: “Blockchain for Insurance Claims Management” discusses how consensus methods, smart contracts, and machine learning algorithms can be used to simplify the claims process, protect claim transactions, and prevent fraud. Furthermore, this chapter also discusses the blockchain-based IoT approach in the insurance domain, along with recent advances in blockchain. Chapter 5: “Industrial Automation: 6G with Artificial Intelligence,” highlights artificial intelligence with 6G in industries. This chapter shows how these technologies are applied in autonomous vehicle networks, cybersecurity, scientific discovery, healthcare applications, industrial security, and event identification by avoiding damage to both human beings and machines. Chapter 6: “A New Deep Learning Approach for Stock Price Prediction Using CNN-BiLSTM and ARIMA-LSTM” demonstrates the algorithms and experimental results, ground-breaking techniques for stock price forecasting, hybrid methods, time series models, and comparison of BiLSTM, CNN, and ARIMA-LSTM with prediction accuracy. Chapter 7: “Unleashing the Power of AI in Business, Finance, Industry 4.0, and Banking 4.0,” gives details about AI, cognitive technology, data analysis, AI algorithms, consumer preference prediction, and sales maximization. This chapter highlights the importance of AI in various sectors like business, finance, banking, retail, insurance, and Banking 4.0, etc. Chapter 8, “Applications of Quantum Machine Learning in Financial Services,” highlights the quantum machine learning approaches for financial services, such as fraud detection, prediction, credit scoring, and stock market forecasting. Chapter 9: “Human Machine Collaboration in Business Processes” offers content comprehension as an application of human machine collaboration in business processes. The chapter has made a point of emphasizing how important it is to design interfaces while taking into account the cognitive and physical limitations of people. In order to achieve a higher score of human-device interaction, the prosperous utilization of human-computer interaction is discussed. Chapter 10, “Amalgamation of AI and Blockchain in Auditing,” analyzes the potential effects of blockchain technology on accounting in general and AI-enabled auditing in particular. Along with this, this chapter also encourages stakeholders like consultants to collaborate on scheming blockchain ecologies that are appropriate for secretarial and audit as those who alter cloud-based accounting. Chapter 11: “Prominent Portrayal of AI Accompanying Transpiring Technologies in Business and Finance” elaborates on the present scenario of AI in financial and business managerial services. This chapter amalgamates the productive relationship between AI and other emanating technologies with respect to business and finance management. Chapter 12: “The Collaboration Between Humans and Machines,” highlights automation and human-machine collaboration and concludes that it can greatly improve efficiency, accuracy, and productivity in various industries and tasks. Chapter 13: “Application of Data Science and Artificial Intelligence in Retail Sector,” presents the application of data science, big data, and the use of AI in the retail sector, store management, one-click ordering, anticipatory shipping, and customer service. Chapter 14: “AI and Data Science in Business Services: Enhancing Efficiency and Driving Innovation” provides a comprehensive overview of AI and data science in business services, predictive analytics, customer sentiment analysis, and advanced data visualization techniques and examines the various applications of these technologies and their impact on the industry.
1.  Amalgamation of Artificial Intelligence (AI) and Data
Science in Taxation ..........................................................................................1
Dhiraj Sharma, Haile Anteneh Yihunie, and Nitish Pathak
2. A Retrospection of Business Intelligence in the Era of Big Data Analytics and Artificial Intelligence for Fintech ........................................15
Rachana Jaiswal
3.  From Data to Insights: How AI and IoT Are Reshaping
Financial Services? ........................................................................................45
Neha A. Kantikar and P. Nandish
4. Blockchain for Insurance Claims Management .........................................65
Masooda Modak, Kalyani Pampattiwar, Namrata Patel, and Neelam Sharma
5.  Industrial Automation: 6G with Artificial Intelligence .............................93
S. Maheswaran, R. D. Gomathi, S. Sathesh, S. Nandita, I. Mohammed Shafiq,
P. Rithika, and Y. Syamala
6.  A New Deep Learning Approach for Stock Price Prediction
Using CNN-BILSTM and ARIMA-LSTM ...............................................127
Priyanka Mahajan, Prabhpreet Kaur, and Neelam Sharma
7.  Unleashing the Power of AI in Business, Finance,
Industry 4.0, and Banking 4.0 ....................................................................157
Shallu Sehgal, Nitish Pathak, and Priyanka Garg
8.  Applications of Quantum Machine Learning in Financial Services......175
S. Bhuvaneswari, A. Abirami, R. Deepakraj, Jyoti Batra Arora, and K. Kaarthic Varun
9.  Human-Machine Collaboration in Business Processes ...........................195
Brijmohan Daga, Nitish Pathak, Srushti Suraj Shah, Austin Bernard Fernandes,
Jayden Jude Dsouza, and Gayatri Pramod Varma
Contents
viii Contents
10. Amalgamation of AI and Blockchain in Auditing ....................................223
Pawan Whig, Shama Kouser, Ashima Bhatnagar Bhatia,
Rahul Reddy Nadikattu, and Radhika Mahajan
11. Prominent Portrayal of AI Accompanying Transpiring
Technologies in Business and Finance .......................................................245
Pooja Singh, Seema Shokeen, Samiksha Garg, and Jyoti Batra Arora
12.  The Collaboration Between Humans and Machines ...............................275
Richa Vijay and Arvind Kumar
13.  Application of Data Science and Artificial Intelligence in
Retail Sector.................................................................................................291
Kiran Menghani and Nitish Pathak
14.  AI and Data Science in Business Services: Enhancing
Efficiency and Driving Innovation .............................................................313
Abdo H. Guroob and D. H. Manjaiah
Index .....................................................................................................................339