The advancement in FinTech especially artificial intelligence (AI) and machine learning (ML), has significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are getting more complex in the field of finance. ML is used in many financial companies which are making a significant impact on financial services. With the increasing complexity of financial transaction processes, ML can reduce operational costs through process automation which can automate repetitive tasks and increase productivity. Among others, ML can analyze large volumes of historical data and make better trading decisions to increase revenue. This book provides an exhaustive overview of the roles of AI and ML algorithms in financial sectors with special reference to complex financial applications such as financial risk management in a big data environment. In addition, it provides a collection of high-quality research works that address broad challenges in both theoretical and application aspects of AI in the field of finance.

Social financial reporting as an economic tool presents the firm as a socio-economic unit with empowered social capital to enable a sustainable economic solution, particularly in response to the COVID-19 pandemic. Islamic social finance (ISF) is a corporate social responsibility initiative in the form of humanitarian and socio-development programs by Islamic financial institutions and Shariah-compliant corporations. ISF is applied through various methods and tools that structure based on Islamic Sharia Law. For example, Islamic social finance tools would either be philanthropic, involving activities such as zakat (obligatory alms-giving), Sadaqah (voluntary alms-giving/charity), and waqf (endowment) or ta'awun (cooperation-based activities), which include Qardh al-hasan (benevolent loan) and kafala (guarantee). Thus, Islamic social finance instruments play a vital role in alleviating poverty and addressing socio-economic issues such as illiteracy, unemployment, malnutrition, and health issues. As such, integrated ISF reporting can empower sustainable economic development and lead to recovery. The Handbook of Research on Islamic Social Finance and Economic Recovery After a Global Health Crisis provides insights on the role of Islamic social finance in supporting and facilitating economic recovery in the post-COVID-19 era as well as reducing poverty and addressing the challenges of socio-economic problems such as education, unemployment, malnutrition, and health issues. This book is ideally intended for practitioners, stakeholders, researchers, academicians, and students who are interested in improving their understanding on the role of Islamic social finance theoretically and empirically in solving the issue of poverty and developing excellent funds management to achieve economic empowerment with better environmental sustainability.

The advancements in artificial intelligence and machine learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are becoming more complex within the field of finance. Artificial intelligence and machine learning, with their spectacular success accompanied by unprecedented accuracies, have become increasingly important in the finance world. Advanced Machine Learning Algorithms for Complex Financial Applications provides innovative research on the roles of artificial intelligence and machine learning algorithms in financial sectors with special reference to complex financial applications such as financial risk management in big data environments. In addition, the book addresses broad challenges in both theoretical and application aspects of artificial intelligence in the field of finance. Covering essential topics such as secure transactions, financial monitoring, and data modeling, this reference work is crucial for financial specialists, researchers, academicians, scholars, practitioners, instructors, and students.