M. Kabir Hassan – författare

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.

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.

It is said that the COVID-19 pandemic has turned back the poverty clock. As such, there is a need to have social mechanisms put in place to provide relief to those who are affected in this regard. Islamic social finance consists of tools and institutions that could be used to alleviate poverty. This book explores the impact of COVID-19 on Islamic finance to better understand the effectiveness of Islamic social finance in helping those who have been affected by poverty overnight due to the halt in all major economic activities in the context of the pandemic.

Since the struggle against poverty in each country will be different, the book attempts to shed light on the experiences of different countries by presenting successful models of Islamic social finance. The book first looks at poverty and COVID-19 before delving into the role of Islamic social financial institutions and how they have risen against COVID-19. The book concludes by examining the impact of COVID-19 on Islamic microfinance.

This book is the first of its kind on the subject of COVID-19, and it intends to bridge the gap in the literature.

It is said that the COVID-19 pandemic has turned back the poverty clock. As such, there is a need to have social mechanisms put in place to provide relief to those who are affected in this regard. Islamic social finance consists of tools and institutions that could be used to alleviate poverty. This book explores the impact of COVID-19 on Islamic finance to better understand the effectiveness of Islamic social finance in helping those who have been affected by poverty overnight due to the halt in all major economic activities in the context of the pandemic.

Since the struggle against poverty in each country will be different, the book attempts to shed light on the experiences of different countries by presenting successful models of Islamic social finance. The book first looks at poverty and COVID-19 before delving into the role of Islamic social financial institutions and how they have risen against COVID-19. The book concludes by examining the impact of COVID-19 on Islamic microfinance.

This book is the first of its kind on the subject of COVID-19, and it intends to bridge the gap in the literature.

This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data.

Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data.

Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.