Data Analytics and AI for Quantitative Risk Assessment and Financial Computation is an all-encompassing reference for finance professionals, risk managers, data scientists, and students seeking to leverage the transformative power of AI and data analytics in finance. The book encapsulates this integration's theoretical underpinnings, practical applications, challenges, and future directions, empowering readers to enhance their analytical capabilities, make informed decisions, and stay ahead in the competitive financial landscape.
The book provides a structured approach that covers foundational topics in quantitative risk assessment, data analytics, and AI, providing a roadmap for professionals to navigate the complexities of integrating AI and data analytics into financial practices. The book explores specific applications and methodologies, including machine learning algorithms for economic modeling and AI-driven strategies for risk management, providing readers with practical insights and strategies for success in the AI-driven financial future. With this book as a guide, professionals can confidently embrace the power of AI and data analytics to stay ahead in the ever-evolving economic landscape.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
•AI Ethics and Bias in Financial Models
•Algorithmic Trading and High-Frequency Trading (HFT)
•Artificial Intelligence and Beyond
•Big Data Technologies in Finance
•Blockchain and Cryptocurrencies Risk Assessment
•Credit Risk Modeling and Assessment
•Derivative Pricing
•Fundamentals of Data Analytics and AI
•Liquidity Risk Measurement and Management
•Machine Learning Algorithms for Financial Modeling
•Market Risk Analysis and Value at Risk (VaR) Models
•Natural Language Processing (NLP) in Financial Analysis
•Operational Risk Management
•Portfolio Optimization and Asset Allocation
•Probability Theory and Statistical Analysis for Risk Assessment
The book provides a structured approach that covers foundational topics in quantitative risk assessment, data analytics, and AI, providing a roadmap for professionals to navigate the complexities of integrating AI and data analytics into financial practices. The book explores specific applications and methodologies, including machine learning algorithms for economic modeling and AI-driven strategies for risk management, providing readers with practical insights and strategies for success in the AI-driven financial future. With this book as a guide, professionals can confidently embrace the power of AI and data analytics to stay ahead in the ever-evolving economic landscape.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
•AI Ethics and Bias in Financial Models
•Algorithmic Trading and High-Frequency Trading (HFT)
•Artificial Intelligence and Beyond
•Big Data Technologies in Finance
•Blockchain and Cryptocurrencies Risk Assessment
•Credit Risk Modeling and Assessment
•Derivative Pricing
•Fundamentals of Data Analytics and AI
•Liquidity Risk Measurement and Management
•Machine Learning Algorithms for Financial Modeling
•Market Risk Analysis and Value at Risk (VaR) Models
•Natural Language Processing (NLP) in Financial Analysis
•Operational Risk Management
•Portfolio Optimization and Asset Allocation
•Probability Theory and Statistical Analysis for Risk Assessment