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Paperback. Condizione: new. Paperback. Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Hardcover. Condizione: new. Hardcover. Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: new. Paperback. Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

Paperback. Condizione: new. Paperback. Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. …

Hardcover. Condizione: new. Hardcover. Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Hardcover. Condizione: new. Hardcover. Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

Taschenbuch. Condizione: Neu. Implementing AI-ML Systems for Credit Risk Analytics in Regulated Banks | Saurabh Kakkar | Taschenbuch | Englisch | 2026 | Notion Press | EAN 9798906435248 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Implementing AI/ML Systems for Credit Risk Analytics in Regulated Banks presents a comprehensive guide to designing, implementing, and governing artificial intelligence (AI) and machine learning (ML) solutions for credit risk management in modern banking. Covering the complete AI/ML lifecycle, the book examines credit risk modeling, regulatory compliance, data engineering, feature engineering, model development, explainable AI, validation, deployment, and MLOps within highly regulated financial environments. It explores key regulatory frameworks, including Basel III, IFRS 9, and SR 11-7, while addressing ethical AI, fairness, bias mitigation, governance, and audit readiness. The book also highlights emerging innovations such as federated learning, AI-native data architectures, data mesh, digital twins, sustainable computing, and quantum computing. Combining technical depth with practical implementation strategies and regulatory best practices, this book is an invaluable resource for banking professionals, risk managers, data scientists, compliance officers, researchers, and students building transparent, scalable, compliant, and trustworthy AI-driven credit risk systems.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma Shruti KakkarDr Shruti Kakkar Sharma completed her Post Graduation in 2013 & since then has presented, published and won accolades in many state, national and international level papers in various conferences and conventions. .…

Buch. Condizione: Neu. Implementing AI-ML Systems for Credit Risk Analytics in Regulated Banks | Saurabh Kakkar | Buch | Englisch | 2026 | Notion Press | EAN 9798906435255 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…