Preface

Computational Modelling Applications have brought revolution in the field of finance and this book explores the market dynamics by gaining insights from experts in the field of finance, investment, computer technology, and entrepreneurship.

This book provides a pragmatic overview of various computer technologies that have made drastic advances in the field of finance resulting in reduced cost, automation, transparency, and reduced fraud in the field of financial services. Fintech has resulted in better customer experience.

The above book talks about computer technology and finance. The book considers the scenarios with technology augmentations of AI/ML in financial services. Few chapters also include case studies related to the above-mentioned topics and provide readers with a glimpse of how beneficial it would be to integrate technology into financial services to make them more customer-friendly and safe additionally making it profitable for businesses.

This book discusses computational modeling approaches such as resource-based view, agent-based modelling, system dynamics, machine learning, network analysis, data granular approach, credit scoring models, logistic regression, Monte Carlo simulations, machine learning algorithms, Value at Risk (VaR) models, stress testing, scenario analysis, simulation models, scenario analysis, fault tree analysis, machine learning for anomaly detection, liquidity gap analysis, cash flow forecasting, anomaly detection algorithms, machine learning models, statistical analysis, computational risk modeling, ResNet-50, and CoViaR.

This book comprises 17 chapters and covers topics like socio-cognitive approach, greenwashing, predictive analytics in insurance, portfolio risk, financial risk management, behavioral finance, data granular approach, green bond market, green finance, green securities market, sustainable financing, and sustainable banking goals.

Chapter 1 explores interrelations with trade, industrialization, capital formation, and environmental regulations to establish their overall effect on sustainable finance—in particular, how FinTech works in resource management optimization through human capital.

Chapter 2 investigates the complexities of green security issuance, the difficulties presented by unfriendly choices, the essential drivers of market extension, and the developing techniques utilized by financial backers.

Chapter 3 establishes the relation between these two aspects i.e. computational modelling approaches to fintech and financial inclusion, and also aims to identify mediating variables.

Chapter 4 develops a socio-cognitive roadmap that mitigates information asymmetry and greenwashing, thereby improving the efficacy of sustainable finance.

Chapter 5 has three main sections. An overview of the green bond market, discussing various types along with their issuance levels and geographical distribution, is discussed in the first section. The second section discusses the reasons for the green bond market growth and the challenges that impede its development. The final section discusses specific computational models applied to green bonds in detail.

Chapter 7 explores the Resource Based View (RBV) theory and draws findings from a qualitative case-based approach to present the implications of predictive analytics in the insurance industry.

Chapter 8 proposes a hybrid risk predictive model. It uses a combination of ResNet-50 (to analyze and quantify spatial image data) and CoViaR (risk prediction) models.

Chapter 9 utilises computational approaches to evaluate how fintech solutions influence women's financial autonomy in banking.

Chapter 10 explores the computational modeling approaches for sustainable finance like blockchain, etc.

Chapter 11 discusses the integration of machine learning, network analysis, and other techniques to enhance risk identification, scenario analysis and decision support in financial institutions.

Chapter 12 examines the motivation and strategies essential for supporting and developing the FinTech sector in India.

Chapter 13 explores the link between behavioural finance characteristics and investing choices, specifically examining the role that is gender-specific.

Chapter 14 contributes to the growing body of research on green finance and sustainable investing, providing valuable insights into the midterm risk-return dynamics of green bonds.

Chapter 15 contributes to sustainable HR by establishing that sustainability can be driven by technology, values, and engagement of the employees.

Chapter 16 discusses green energy, and energy financing as a lot of concerns related to climate change are coming to light.

Chapter 17 discusses the significance of hearty norms and administrative structures to keep up with market believability and advance certified ecological effects through green securities.