چگونه خسارات اعتباری مورد انتظار را برای IFRS 9 و CECL مدل‌سازی و اعتبارسنجی کنیم: راهنمایی عملی با مثال‌های کار شده در R و SAS

How to Model and Validate Expected Credit Losses for IFRS 9 and CECL: A Practical Guide with Examples Worked in R and SAS covers a hot topic in risk management. The IFRS 9 expected credit loss accounting principle (going live in 2018) and the US CECL standard (going live in 2020) require creditors to adopt a new perspective in assessing their credit exposures. The book explores the best modeling process, including the most common statistical techniques used in estimating expected credit losses. A practical Excel-based approach encourages non-technical professionals to grasp the key concepts required to understand, challenge and validate these models. Additionally, the reader with broader modeling experience will benefit from a more technical dissertation accompanied with cases worked in SAS and R (the software packages most commonly used by credit risk managers to develop their models). Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit vehicles Concentrates on specific aspects of the model, with each chapter building upon earlier chapters Provides a non-technical approach to enable readers to perform the review, validation and audit of models __How to Model and Validate Expected Credit Losses for IFRS 9 and CECL: A Practical Guide with Examples Worked in R and SAS__covers a hot topic in risk management. The IFRS 9 expected credit loss accounting principle (going live in 2018) and the US CECL standard (going live in 2020) require creditors to adopt a new perspective in assessing their credit exposures. The book explores the best modeling process, including the most common statistical techniques used in estimating expected credit losses. A practical Excel-based approach encourages non-technical professionals to grasp the key concepts required to understand, challenge and validate these models.Additionally, the reader with broader modeling experience will benefit from a more technical dissertation accompanied with cases worked in SAS and R (the software packages most commonly used by credit risk managers to develop their models).Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit vehiclesConcentrates on specific aspects of the model, with each chapter building upon earlier chaptersProvides a non-technical approach to enable readers to perform the review, validation and audit of models IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management. Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit products Concentrates on specific aspects of the modelling process by focusing on lifetime estimates Provides an hands-on approach to enable readers to perform model development, validation and audit of credit risk models "IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management."--Publisher's description IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management. -- Provided by publisher