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{ "item_title" : "Ifrs 9 and Cecl Credit Risk Modelling and Validation", "item_author" : [" Tiziano Bellini "], "item_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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/0/12/814/940/012814940X_b.jpg", "price_data" : { "retail_price" : "94.95", "online_price" : "94.95", "our_price" : "94.95", "club_price" : "94.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Ifrs 9 and Cecl Credit Risk Modelling and Validation|Tiziano Bellini

Ifrs 9 and Cecl Credit Risk Modelling and Validation : A Practical Guide with Examples Worked in R and SAS

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Overview

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.

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Details

  • ISBN-13: 9780128149409
  • ISBN-10: 012814940X
  • Publisher: Academic Press
  • Publish Date: January 2019
  • Dimensions: 9.25 x 7.5 x 0.67 inches
  • Shipping Weight: 1.21 pounds
  • Page Count: 316

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