Guest Editors
Dr. Mohammad Shabaz, Model Institute of Engineering and Technology, India.
Dr. Gaganpreet Kaur, Chitkara University, Punjab, India.
Dr. Abolfazl Mehbodniya, Kuwait College of Science and Technology (KCST), Kuwait.
Dr. Dinesh Jackson Samuel, The University of Texas MD Anderson Cancer Center, United States.
Summary
Internet of things (IoT) has deeply penetrated our society, and in day to day activities, the impact of IoT is inescapable. Swift digitalization of banking and finance sectors paved the way for the introduction of IoT in leveraging services associated with financial economics and accounting. The big data generated by a diverse set of IoTs enable the introduction of machine learning (ML) and artificial intelligence (AI) modules which aid in making remarkable improvements in financial sectors.
IoT-generated big data improves the security in financial transactions by detecting the anomalies in banking activities by combining biometrics and cryptography in data analytics that remarkably reduces the fraudulent activities to several folds. The adoption of ML modules in big data of banking services gave intelligent decisions in the risk-free making of accurate budgets and payments. Also, the integration of IoT data analytics in insurance and mortgage efficiently manages the assets and reduces the risk level to several folds. In addition, prescriptive analytics of bank services aid in determining potential leads for expansion of business and financial opportunities. Also, IoT data greatly helps offer a more personalized experience of monetary economics and accounting. Thus IoT enabled data analytics are a paradigm shift in the financial sector.
The ubiquitous presence of IoT in the financial sector transforms their performance to next-generation banking. However, potential challenges and pitfalls need to be addressed before global adoption and recommendation. Primarily, the high connectivity of the IoT opens potential opportunities for hacking and misuse, which needs to be addressed. Most of the IoTs are manufactured by companies that do not directly involve financial sectors and provide poor services in many cases that need improvements. Secure storing IoT-generated big data is still inadequate, which needs research advances. The reliability of models generated by IoT-derived data requires careful reassessment. Additionally, potential vulnerabilities in using IoT need to be addressed. However, IoT data analytics would be a productive tool in enhancing financial economics and accounting on substantial improvement. Moreover, IoT-enabled data analytics further explore previously unknown new opportunities in the financial sector, which would be disruptive.
Therefore this special issue aims to discuss and highlight financial services, banking, mortgage, mutual funds, money transfer, data analytics, machine learning, artificial intelligence, security, and privacy issues. We invite researchers from various fields to present their research work, review, and perspectives in IoT Data Analytics for Financial Economics and Accounting.
Topics of interest to the Special Issue include but are not limited to:
1. Impact of ML methods, techniques in the convergence of banking and business practices
2. Advances in architecture for the assisted autonomous recommendations in financial risk management
3. Emerging research in IoT enabled big data for inventory tracking and management of bank transactions
4. Innovations in enhancing personalization of customer services and marketing
5. Impact of sensing technologies and D2D communication in decision making of asset management
6. Innovations in IoT enabled the real-time gathering of big data and client management
7. Research in the tracking of customer services in leveraging smarter interactions
8. Advances in centralized monitoring and swift thief intrusion in financial services
9. Emerging advances in the development of secure ways to store and retrieve IoT derived big data for intelligent decision making
10. Trends in a technology-enabled network of all networks for automated business transactions
11. Leveraging every financial service with the adoption of smarter IoT applications
12. Advances in the integration of defensive policy management for detection and intrusion of fraudulent activities
13. Enacting policies and laws to increase cyber security in securing user privacy and security
Keywords
IoT; Banking; Machine Learning(ML); Financial; Accounting
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