Chapter 11 Conclusion – Artificial Intelligence for Risk Management

CHAPTER 11

Conclusion

The use of artificial intelligence (AI) is becoming prolific these days. One can hear about AI on television, radio, and the Worldwide Internet. The risk has been a topic in everyone’s mind and continues to hurt organizations and individuals in the international corporate world, as well as domestically. Here, we recapitulate important reminders that will benefit those in all walks of life.

Risk is not fun to experience. Organizations that have endured risk do not take them lightly. We can go on and on discussing the negative impacts risk has created. We graphically described vivid areas impacted by risk in this book. Do not turn your face away from risks or the negative impacts that come with them. We discussed natural risk at length; natural risk cannot be stopped but can be mitigated to save lives. It is worth paying careful attention to the steps that humans have available to mitigate natural risks.

It is important that risks are identified early and in a timely manner to minimize the impact of damage or fatal situations. Based on discussions laid out in this book, the reader can take advantage of using the tools presented. Risks usually require risk management to safely cope with the situation. The data collected must be used carefully. This may mean going through checklists to ensure appropriate questions are answered and appropriate immediate steps taken, based on the immediate answers. Action may include capturing historical data on risk issues. Once accurate data accrue, the next step is to analyze them carefully and accurately. Captured data may help predict the future occurrence of risk issues. This approach may help salvage nasty situations that can be damaging and expensive to individuals or organizations.

Because risk issues are important, it is important for organizations to understand the hybrid types. Careful analysis will provide accurate direction. We chose AI to provide directions. However, AI requires accurate inputs to gain accurate outputs. The source of data and quality of data are crucial in every way. This book provides various use cases that may be helpful to organizations in properly strategizing the route to take. Organizations must define, analyze, monitor, control, and mitigate risks.

Because humans cannot analyze a huge amount of data or take a long time to process data, data science, data analytics, and machine learning (ML) algorithms are used to analyze data that ultimately will be used as input to determine corrective actions. AI is a legitimate way to carry out all the necessary determinations. Using machines to learn from previous human experiences as data input and enables continuous learning from new sets of input data, based on the development of mathematical algorithms leading to the creation of ML. Subsequently, we used AI in this book. We carried out complete AI system development to clearly illustrate how to process risk data. We provide recommendations for the most effective methods. The methods of data collection and development in this book offer the reader ways to understand how to tackle use cases such as AI and risk in an organization. AI produces effective and dramatic results in business, and organizations desiring to understand and improve risk management skills can use AI to improve their chances of handling risk.

Much evidence has shown that risk management has become important everywhere. Case studies in this book illustrated and supported the use of large volumes of data, different velocities, and varieties of data from various sources. We assess that risk concerns will not stop soon. Rather, they appear to be growing larger and more frequent with sometimes massive negative impact. An undetermined positive risk can also hurt organizations when it comes to business opportunities leading to a loss of revenue. In general, the range and breadth of risk creates havoc across the world and on a variety of projects. We show the importance of risk management in organizations in many sections of this book. The harm of risk, without appropriate management, can be devastating. Using the correct tools can address or prevent that devastation.

We focused much effort on problem statements with appropriate use cases and proposals for use of AI solutions using data science and ML approaches. The comprehensive description of AI and risk provides concrete answers to crucial questions that so many organizations face: Where are these risks and what can be done to lower their impacts? Is AI part of the answer to risk mitigation? Organizations and individuals may gain much knowledge and shared experiences from this book.

For organizations willing to create their own AI systems for risk, this book will guide them step by step. Additionally, the reader can look at Bizstats.ai for more guidance. Bizstats.ai offers numerous customized AI design tools; individuals and organizations are welcome to contact them. Should the reader need training on the tools used in the implementation of AI and risk, please contact Bizstats.ai.