Ambrish Kumar Mishra – författare

This book explores the multifaceted implications of technological innovation on legal systems, addressing how emerging technologies enhance, challenge, and necessitate reform in legal practice and governance, with a focus on artificial intelligence, big data, and blockchain.Technological advancements have profoundly reshaped every facet of human life, including the realm of law and governance. From automating legal research and drafting to revolutionizing court proceedings through e-court automation, AI-driven systems are transforming the legal landscape. These advancements bring unprecedented efficiency but also pose critical legal, ethical, and technical challenges. By delving into applications such as predictive justice, document mining, online dispute resolution, and AI governance, this book provides insights into the opportunities and complexities of integrating technology into law; and addresses key topics such as automating legal decision-making, safeguarding data privacy, and the ethical use of AI in the justice system. Through in-depth analysis and case studies, this book bridges the gap between technological innovation and regulatory systems, ensuring that the future of law incorporates robust mechanisms to meet emerging challenges.The book will be of interest to researchers in the field of the law of emerging technologies, regulation, governance, artificial intelligence, and data science.

As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations, demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practices is packed with up-to-date examples and data, ensuring it is both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts toward sustainable development, empowering readers to enhance their decision-making abilities, and also discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.

As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations, demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practices is packed with up-to-date examples and data, ensuring it is both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts toward sustainable development, empowering readers to enhance their decision-making abilities, and also discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.

This book explores the multifaceted implications of technological innovation on legal systems, addressing how emerging technologies enhance, challenge, and necessitate reform in legal practice and governance, with a focus on artificial intelligence, big data, and blockchain.Technological advancements have profoundly reshaped every facet of human life, including the realm of law and governance. From automating legal research and drafting to revolutionizing court proceedings through e-court automation, AI-driven systems are transforming the legal landscape. These advancements bring unprecedented efficiency but also pose critical legal, ethical, and technical challenges. By delving into applications such as predictive justice, document mining, online dispute resolution, and AI governance, this book provides insights into the opportunities and complexities of integrating technology into law; and addresses key topics such as automating legal decision-making, safeguarding data privacy, and the ethical use of AI in the justice system. Through in-depth analysis and case studies, this book bridges the gap between technological innovation and regulatory systems, ensuring that the future of law incorporates robust mechanisms to meet emerging challenges.The book will be of interest to researchers in the field of the law of emerging technologies, regulation, governance, artificial intelligence, and data science.

As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations, demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practices is packed with up-to-date examples and data, ensuring it is both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts toward sustainable development, empowering readers to enhance their decision-making abilities, and also discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.

This book explores the multifaceted implications of technological innovation on legal systems, addressing how emerging technologies enhance, challenge, and necessitate reform in legal practice and governance, with a focus on artificial intelligence, big data, and blockchain.Technological advancements have profoundly reshaped every facet of human life, including the realm of law and governance. From automating legal research and drafting to revolutionizing court proceedings through e-court automation, AI-driven systems are transforming the legal landscape. These advancements bring unprecedented efficiency but also pose critical legal, ethical, and technical challenges. By delving into applications such as predictive justice, document mining, online dispute resolution, and AI governance, this book provides insights into the opportunities and complexities of integrating technology into law; and addresses key topics such as automating legal decision-making, safeguarding data privacy, and the ethical use of AI in the justice system. Through in-depth analysis and case studies, this book bridges the gap between technological innovation and regulatory systems, ensuring that the future of law incorporates robust mechanisms to meet emerging challenges.The book will be of interest to researchers in the field of the law of emerging technologies, regulation, governance, artificial intelligence, and data science.

Artificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.

Artificial Intelligence for Risk Mitigation in the Financial Industry

This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability.

The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc.

Audience

This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.