Risk modeling : practical applications of artificial intelligence, machine learning, and deep learning

Table of Contents: 1.Introduction 2.Data Management and Preparation 3.Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management 4.Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models 5.Bias, Fairness, and Vulnerability in Decision-Making 6.Machine Learning Model Deployment, Implementation, and Making Decisions 7.Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring 8.Optimizing Parameters for Machine Learning Models and Decisions in Production 9.The Interconnection between Climate and Financial Instability

A comprehensive review of the application of machine learning and AI techniques in financial risk management, including implementation guidance.This book introduces readers to the application of cutting-edge AI technology for forecasting and assessing financial risks. This practical handbook investigates the new potential and problems connected with applying machine learning and artificial intelligence (AI) in the risk management process. It also provides an up-to-date examination of the actual use of current modelling techniques in risk management. This book depicts how risk modelling is using machine learning and AI techniques to efficiently consume complicated data and address current modelling lifecycle gaps. For risk models and risk practitioners, explains how proprietary software and open-source languages can be blended to provide the best of both worlds