Häftad, Engelska, 2027
Principles and Practices of Model Risk Management (MRM)
1217 kr
Kommande
Beskrivning
- Presents the principles and practice of Model Risk Management (MRM)
- Covers risk management frameworks, regulatory guidelines, model risk types, and tools for identifying, measuring, monitoring, and reporting model risk
- Serves as a valuable resource for advanced undergraduate and graduate students in finance, business, and economics, providing practical insights across disciplines
- Operationalizes risk management principles specifically for model risks, enabling readers to design and implement effective MRM frameworks.
- Includes Jupyter notebooks with standalone examples hosted online, enhancing the learning experience with practical, hands-on materials that complement the theoretical content
Produktinformation
- Utgivningsdatum: 2027-01-01
- Mått: 191 x 235 x undefined mm
- Vikt: 450 g
- Format: Häftad
- Språk: Engelska
- Antal sidor: 400
- Förlag: Elsevier Science
- ISBN: 9780443277337
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Mer om författaren
Charlie Lu is an industry-leader with extensive experience in building model and model risk management areas, including consumer credit cards, wholesale credits, counterparty credits, liquidity stress, IRRBB, market risk, derivatives pricing, operational risk, stress testing, and scenario expansion. Additional modeling experience spans across AI/ML in fraud detection, surveillance and compliance, marketing, valuation, and customer maintenance. He has helped establish respected brand names and served as an ultimate gatekeeper and arbitrager to resolve disagreements in model validation. Recognized as key contributor in building model risk management frameworks, including policy, standards, procedures, risk assessment, and large model frameworks to address aggregated model risk, Charlie Lu has also contributed significantly to regulatory mandates, including CCAR, LST, RRP, IRRBB, and CECL. Peter Russo is a risk professional in the model risk management space with expertise in multiple areas, including operational risk, wholesale credit risk, stress testing, liquidity stress, economic scenario generation, and market risk. Additionally, he has extensive experience in developing production-grade deep learning models for the e-commerce space. He is also a course designer and instructor for the Introduction to Model Risk Management course in Columbia University’s ERM program, which has been offered in the Columbia ERM program since January 2022.
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