Risk Analytics: Data-Driven Decisions under Uncertainty

The 2022 World Economic Forum surveyed 1,000 experts and leaders, indicating that environmental risks are a priority to study in a formal way. Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics.

Format: Hardback
Length: 469 pages
Publication date: 08 August 2023
Publisher: Taylor & Francis Ltd

The 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earths conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in the minds of leaders, new knowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks.

Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human and artificial intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components, such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation, but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation.

This book moves from a review of data to the concepts of a RAS. It reviews RAS system components required for the development of a RAS, including data architecture, data collection, data analysis, and risk modeling. It also discusses the role.

The book also discusses the challenges and opportunities of using data analytics in risk management, including the need for interdisciplinary collaboration, the importance of data quality and integrity, and the need for ethical considerations in the use of data analytics.

Finally, the book provides case studies and examples of successful risk analytics implementations in various industries, including finance, healthcare, and transportation.

In conclusion, Risk Analytics: Data-Driven Decisions Under Uncertainty is a comprehensive and practical guide to developing and implementing a risk analytics system that can help organizations make informed decisions in a rapidly changing world.

Dimension: 254 x 178 (mm)
ISBN-13: 9780367359614