This book provides an overview and introduction to the application of artificial intelligence and machine learning in risk management. It will cover practical application of newer modelling techniques in risk management and explore what the opportunities are of using artificial intelligence and machine learning, as well as the risks and challenges associated with the innovation. In addition, it will explain the options to extend the model governance framework for artificial intelligence and machine learning. Readers will be provided with a clear understanding about the strengths and weaknesses of artificial intelligence and machine learning as it can be applied in risk management and practical steps on how to implement it in their organization. It will: Demystify the use of machine learning and AI techniques as innovative technologies, as well as practical tools that are available to assess bias and interpretability of resultant models developed with these alternative algorithms and techniques. Explain the principles of machine learning algorithms and AI techniques, including the nuances of feature engineering and common algorithms using simple explanations written in plain English. Show how to practically apply the principles of machine learning to everyday risk management problems and projects that require a model to be developed, but how the processes of machine learning can continue to incorporate learnings from decades of prior model builds, subsequent interpretation and validation. Show how quickly modeling is changing and being modernized to incorporate machine learning and AI techniques to more rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle including, but not limited to, data dictionaries and autotuning. Explain how proprietary software and open-source languages can be combined to deliver the best of both worlds for risk models and risk practitioners. Show how to better target specific sub-steps of the model lifecycle for machine learning and AI techniques and those that are indeed better serviced by retaining classical modeling approaches. Explain how machine learning and AI techniques can be applied to topical areas like impact of exogenous events (e.g., a pandemic) and understanding climate change impacts but resolved at a more practical level.

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