The adoption and delivery of financial services today have been profoundly impacted by the development of fintech, particularly in the areas of artificial intelligence (AI) and machine learning (ML). In the realm of finance, crucial decisions regarding investments, macroeconomic analysis, and credit evaluation are becoming more difficult. Many financial institutions use machine learning, which significantly impacts the financial services industry. Because financial transaction processes are becoming more complicated, machine learning (ML) can lower operational costs by automating repetitive procedures and boosting productivity. Among other things, ML can analyze vast amounts of previous data and improve trading decisions to boost profits. Resilience, human centricity, and sustainable development are the focus of Industry 5.0. Nowadays, AI and ML have almost complete influence over all aspects of society.
AI/ML, which includes computational intelligence, deep learning, and reinforcement learning, has emerged as a novel phenomenon that has astounded researchers across practically all fields with its remarkable performance and unmatched accuracy. The return of AI/ML is so prevalent that it could theoretically and empirically solve every problem from any field with amazing results. It was extremely near to, and in some cases even beyond, human intelligence and cognition. The BFSI (banking, finance, and insurance) sector is not exempt from this contemporary tsunami. These technologies comprise computational intelligence, deep learning, reinforcement learning, deep reinforcement learning, and traditional ML-based predictive analytics.
This book's major goal is to comprehensively understand the functions of AI and ML algorithms in the financial sectors, focusing on industry 5.0 concepts like resilience, human centricity, and sustainable development. Additionally, it intends to offer a compendium of excellent research papers that tackle major issues in AI's theoretical and practical applications in the banking industry. We ask coworkers to submit original book chapters that will encourage ongoing work on ML algorithms that help tackle the challenge of huge data processing in a complicated banking and financial environment. Practitioners who are creating algorithms, systems, and applications are also encouraged to discuss their ideas, experiences, and findings.
Tentative Table of Contents
Chapter 1: Opportunities and challenges of AI/ML in finance
Chapter 2: Governing AI/ML and finance as governance.
Chapter 3: The evolution of AI/ML in finance and strategy in Industry 5.0.
Chapter 4: Valuation of AI/ML in finance
Chapter 5: Resilience and finance digital economy: The role of AI/ML in Fintech as leader of the economic driver.
Chapter 6: Fintech trends: Industry 5.0, finance and digital transformation in AI/ML
Chapter 7: AI/ML in Fintech innovation and its application in the banking/finance industry.
Chapter 8: AI/ML in the banking sector and its impact on the stakeholders in the wake of resilience.
Chapter 9: An assessment of the level of adoption of AI/ML in Bank/Finance.
Chapter 10: ML models for smart industrial applications
Chapter 11: AI and ML Fintech ecosystem: a comparison.
Chapter 12: The response of AI/ML in finance to the post-COVID-19 pandemic.
Chapter 14: Finance and Cryptocurrency use of AI/ML: A Systematic Analysis.
Chapter 15: Fintech, Blockchain & Crowdfunding: current landscape & path forward.
Chapter 16: COVID-19 and Finance: An Artificial Intelligence-based FinTech model.
Chapter 17: Deep Learning for financial big data analysis
Chapter 18: AI applications for financial risk management
Tentative Topics of Coverage
Chapters are invited on the following themes but will not be restricted to:
1. Financial Monitoring
2. Making Investment Predictions
3. Human Centric Approach
4. Secure Transactions
5. Resilience
6. Algorithmic Trading
7. Financial Advisory
8. Customer Data Management
9. Decision-Making
10. Customer Service Level Improvement
11. Customer Retention Program
12. Sustainable Development
13. Risk management
14. Financial Big Data Analysis
15. Blockchain and smart-contracts