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This chapter is excerpted from 'Artificial Intelligence for Risk Management'. The volcano disaster in Hawaii was uncontrollable (Wall Street Journal May 2018) and loss of market shares led to a collapse in the economy of the environment (Pelling et al 2002; Eddie Guidry et al 2013). These kinds of uncertainties require some approach to mitigate the situation. Experts are required to identify the associated risks early enough to safeguard the situation and minimize the impact. Here, risk management plays a major role in situations of uncertainty. Relevant questions are: How can this situation be forecasted and taken care of early in similar situations? Can these occurrences be captured historically? Can such patterns of occurrences be identified? Can the data relating to the occurrences be captured? Can the data be used to predict future occurrences? Can the relevance of the data be an important factor? Is security important? What happens if the data are manipulated? How are the data manipulated? How can the data be protected and secured? Security takes an important role in the data and is the driving factor in risk management. Various types of risk apply to various industries, various business functions, roles, and responsibilities. This book intends to illustrate top business cases and use cases that apply to respective industries by suggesting ways to define, analyze, monitor, control, and mitigate risk. The importance of this approach is to mitigate risk using data and putting corrective action in place. Because humans cannot quickly analyze huge amounts of data, such analysis takes a long time. People can use data science, data analytics, and machine learning (ML) algorithms to speed the process. Artificial intelligence (AI) enables machines to learn from previous human experiences through data inputs and enables continuous learning from new sets of input data. The development of mathematical algorithms has led to the marked creation of ML, and subsequently to the AI revolution today. In this Artificial Intelligence for Risk Management book, we use AI to mitigate risks through various case studies that will help the reader understand and benefit. AI produces effective and dramatic results in business. Many organizations desire to understand and improve risk management skills to improve their chances of handling risk. Recently, risk has become important everywhere because of the large volume of data, different velocity, and variety of data. These aspects of life appear to be growing bigger and more frequent, often accompanied by negative impacts. People can use an undetermined amount of risk to strengthen their position. The range and breadth of risk creates havoc everywhere in the world, and on a variety of projects. Risk management is important in an organization because without it, the organization may have trouble defining its objectives. However, the most important strategy implementation is fear of financial loss. This book focuses on problem statements with appropriate use cases and proposes AI solutions using data science and machine learning approaches. In this book, we hope to provide concrete answers to the crucial questions facing so many organizations: Where are these risks and what can be done to lower their impacts? Is AI part of the answers to the risk mitigations?