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2 produkter
2 produkter
1 031 kr
Kommande
A First Course in Model Validation and Model Risk Management offers robust coverage for current and future financial engineers. Useful as part of a masters program, for self-study, or as a valuable reference, the textbook explains in step-by-step, practical terms how mathematical models owned by financial institutions are essential to their public activities, including sales, trading, risk management, and internal audits. Like a diverse fleet of cars maintained by a rental car location, a bank must make sure customers can "drive" any of its models for a specific financial product. The book covers both pricing and risk models. Chapters consider modeling basics, marked-to-market and marked-to-model asset classes, market risk, credit risk, portfolio risk, operational risk, capital model risk, and financial crime, along with machine learning/AI.To support course use and practical applications, the text provides examples in Python throughout, as well as an appendix containing homework problems for all chapters, further supported by an ftp site for data and sample code. Additional appendices cover global model risk management, and a refresher in statistics.Offers practical concepts for learning model validation and model risk managementExplains how to use Python-based models to assess and manage model riskCovers the US gold standard of model risk, "Federal Reserve Board SR 11-7", including testing inputs, testing outputs, benchmarking, outcomes analysis, third party models, and compensating controlsDiscusses model governance, including model inventory , risk ratings, and the three lines of defenseProvides Instructor Manuals for qualified instructors via https://www.educate.elsevier.com/book/details/9780443337468
Validating Financial Crime Risk Models
A Guide to Managing Data Quality, Transaction Monitoring, and Compliance
Inbunden, Engelska, 2026
1 659 kr
Kommande
This book is a comprehensive guide to the validation of models used in fraud detection and anti-money laundering (AML) compliance for financial professionals. Focusing on the critical aspects of data quality, transaction monitoring, and customer risk rating, this book equips readers with the knowledge and tools to ensure that their models meet regulatory standards and effectively mitigate financial crime risk.As financial crimes become more and more sophisticated, there is a growing need for a specialized resource that addresses both the technical and regulatory challenges financial managers are facing . Offering a framework, tools, and case examples as well as incorporating the role of AI, this book helps readers ensure that their models are robust, reliable, and capable of detecting and preventing financial crimes effectively.