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.
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
The objective of this paper is to present an integrated tool suite for IFRS 9- and CECL-compatible estimation in top-down solvency stress tests. The tool suite serves as an illustration for institutions wishing to include accounting-based approaches for credit risk modeling in top-down stress tests.
Deep Credit Risk - Machine Learning in Python aims at starters and pros alike to enable you to: - Understand the role of liquidity, equity and many other key banking features- Engineer and select features- Predict defaults, payoffs, loss rates and exposures- Predict downturn and crisis outcomes using pre-crisis features- Understand the implications of COVID-19- Apply innovative sampling techniques for model training and validation- Deep-learn from Logit Classifiers to Random Forests and Neural Networks- Do unsupervised Clustering, Principal Components and Bayesian Techniques- Build multi-period models for CECL, IFRS 9 and CCAR- Build credit portfolio correlation models for VaR and Expected Shortfall- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code- Access real credit data and much more ...
The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.
Stress Testing and Risk Integration in Banks provides a comprehensive view of the risk management activity by means of the stress testing process. An introduction to multivariate time series modeling paves the way to scenario analysis in order to assess a bank resilience against adverse macroeconomic conditions. Assets and liabilities are jointly studied to highlight the key issues that a risk manager needs to face. A multi-national bank prototype is used all over the book for diving into market, credit, and operational stress testing. Interest rate, liquidity and other major risks are also studied together with the former to outline how to implement a fully integrated risk management toolkit. Examples, business cases, and exercises worked in Matlab and R facilitate readers to develop their own models and methodologies. - Provides a rigorous statistical framework for modeling stress test in line with U.S. Federal Reserve FRB CCAR (Comprehensive Capital Analysis Review), U.K. PRA (Prudential Regulatory Authority), EBA (European Baning Authorithy) and comply with Basel Accord requirements - Follows an integrated bottom-up approach central in the most advanced risk modelling practice - Provides numerous sample codes in Matlab and R
Contains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modelin
A better development and implementation framework for credit risk scorecards Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data. Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include: Following a clear step by step framework for development, implementation, and beyond Lots of real life tips and hints on how to detect and fix data issues How to realise bigger ROI from credit scoring using internal resources Explore new trends and advances to get more out of the scorecard Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results.
Reverse stress testing was introduced in risk management as a regulatory tool for financial institutions more than a decade ago. The recent Covid-19 crisis illustrates its relevance and highlights the need for a systematic re-thinking of tail risks in the banking sector. This book addresses the need for practical guidance describing the entire reverse stress testing process. Reverse Stress Testing in Banking features contributions from a diverse range of established practitioners and academics. Organized in six parts, the book presents a series of contributions providing an in-depth understanding of: Regulatory requirements and ways to address them Quantitative and qualitative approaches to apply reverse stress testing at different levels – from investment portfolios and individual banks to the entire banking system The use of artificial intelligence, machine learning and quantum computing to gain insights into and address banks’ structural weaknesses Opportunities to co-integrate reverse stress testing with recovery and resolution planning Governance and processes for board members and C-suite executives Readers will benefit from the case studies, use cases from practitioners, discussion questions, recommendations and innovative practices provided in this insightful and pioneering book.
