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The financial industry has been adopting AI and machine learning at a rapid pace, and Al Risk Management provides the information they need to excel. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. As more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing, and scaling models. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages, and data sets that are entering the enterprise. With little formal guidance from regulators on how to validate models and quantify model risk, organizations are developing their own home-cooked methods to address model risk management challenges.
In this book, the author brings clarity on some of the model risk management and validation challenges with data science and machine learning models in the enterprise. He discusses key drivers of model risk in today’s environment and how the scope of model risk management is changing. He introduces key concepts and discuss aspects to be considered when developing a model risk management framework incorporating data science techniques and machine learning methodologies in a pragmatic way.
You will learn:
· Role of Machine Learning and AI in financial services
· Model Risk Management challenges and best practices for machine learning models
· Validating machine learning models: Quantifying risk, best practices, and templates
· Regulatory guidance and the future
· Case studies with sample code
