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

Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Roberts, Terisa (Autor), Tonna, Stephen J. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley, [2022]
Edición:First edition.
Colección:Wiley and SAS business series.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Risk modeling :  |b practical applications of artificial intelligence, machine learning, and deep learning /  |c Terisa Roberts, Stephen J. Tonna. 
250 |a First edition. 
264 1 |a Hoboken, NJ :  |b Wiley,  |c [2022] 
300 |a 1 online resource (xiv, 186 pages) :  |b illustrations 
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504 |a Includes bibliographical references and index. 
505 0 0 |t Introduction --  |t Data Management and Preparation --  |t Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management --  |t Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models --  |t Bias, Fairness, and Vulnerability in Decision-Making --  |t Machine Learning Model Deployment, Implementation, and Making Decisions --  |t Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring --  |t Optimizing Parameters for Machine Learning Models and Decisions in Production --  |t The Interconnection between Climate and Financial Stability. 
520 |a Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. 
588 |a Description based on print version record and CIP data provided by publisher; resource not viewed. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Risk management. 
650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
650 6 |a Gestion du risque. 
650 6 |a Intelligence artificielle. 
650 6 |a Apprentissage automatique. 
650 7 |a risk management.  |2 aat 
650 7 |a artificial intelligence.  |2 aat 
650 7 |a Artificial intelligence  |2 fast 
650 7 |a Machine learning  |2 fast 
650 7 |a Risk management  |2 fast 
700 1 |a Tonna, Stephen J.,  |e author. 
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830 0 |a Wiley and SAS business series. 
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