Expert Systems in Finance (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
14
Utgivningsdatum
2020-12-18
Förlag
Routledge
Antal komponenter
1
ISBN
9780367729011

Expert Systems in Finance

Smart Financial Applications in Big Data Environments

av Noura Metawa, Mohamed Elhoseny, Aboul Ella Hassanien, M Kabir Hassan
Häftad,  Engelska, 2020-12-18
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Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures. In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent. This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.
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Noura Metawa, Mohamed Elhoseny, Aboul Ella Hassanien, M Kabir Hassan

Övrig information

Noura Metawa is currently an Assistant Professor of Finance, Regis University, USA, and a Lecturer at the Faculty of Commerce, Mansoura University, Egypt. Mohamed Elhoseny is currently an Assistant Professor at the Faculty of Computers and Information, Mansoura University, Egypt. Aboul Ella Hassanien is a Professor at Cairo University, Egypt, Faculty of Computers and Information, IT Department, and the Chair of the Technology Center of Blind and Visual Impaired People. M. Kabir Hassan is a Professor of Finance in the Department of Economics and Finance in the University of New Orleans, LA, USA.

Innehållsförteckning

1. Theoretical and practical foundations of liquidity-adjusted value-at-risk (LVaR): optimization algorithms for portfolio selection and management 2. Financial analysis for mobile and cloud applications 3. Eye-movement study of customers on video advertising marketing 4. An optimization algorithm and smart model for periodic capacitated arc routing problem considering mobile disposal sites 5. Opinion mining analysis of e-commerce sites using fuzzy clustering with whale optimization techniques 6. Big data text mining in the financial sector 7. CEL: citizen economic level using SAW 8. The investment opportunities for building smartphone applications for tourist cities in Saudi Arabia: the case of Abha City 9. An applied credit scoring model 10. Intelligent distributed applications in e-commerce and e-banking 11. Feature selection-based data classification for stock price prediction using ant-miner algorithm 12. The value of simulations characterizing classes of symbiosis: ABCs of formulation design 13. Application of project scheduling in production process for paddy cleaning machine by using PERT and CPM techniques: case study 14. The management of deep learning algorithms to enhance momentum trading strategies during the time frame to quick detect market of smart money 15. Pattern to build a robust trend indicator for automated trading

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